Patent Grant
11863305
Today's blockchain processing consumes great hardware, network, and energy resources. When Satoshi first proposed a cryptographic blockchain, so-called “miners” expended CPU time and electricity to mine blockchain data. The mining of blockchains was democratic, meaning anyone with a conventional CPU-based computer could process the complicated calculations required to embed a block of data on a blockchain. Soon, though, the miners realized that a graphics processing unit (or GPU) was much faster than a CPU and could be optimized to solve the complicated calculations. Soon thereafter, most or all blockchain mining was performed by a specially programmed GPU computer, as a conventional CPU-based computer was comparatively too slow. Today, though, the miners use a specially-designed application-specific integrated circuit (or ASIC), as ASICs are even faster than GPUs. These ASIC computers are much faster at solving the complicated calculations, but the ASIC computers are very expensive and consume large amounts of electrical power. The ASIC computers are so cost prohibitive that, today, blockchain mining is largely undemocratic. Only a relatively small number of miners have access to the financial capital and to energy sources to mine blockchains.
Exemplary embodiments encourage blockchain miners to use CPU-based computer machines. Exemplary embodiments discourage or deter the use of specialized hardware (such as GPUs and ASICs) in blockchain mining by utilizing memory size restrictions and cache latency of cache memory. Exemplary embodiments may further promote democratic mining by separating hashing operations from difficulty and proof-of-work operations. When blockchain transactions or other data is processed or mined, encryption (such as a hashing algorithm) may be a stand-alone software application or programming code. Blockchain miners may also use a separate difficulty scheme and a separate proof-of-work scheme. The encryption/hashing algorithm, a difficulty algorithm, and a proof-of-work algorithm may thus be separately called or executed. A blockchain may thus use any encryption algorithm, any difficulty algorithm, and/or any proof-of-work algorithm. Blockchain environments may thus mix-and-match different encryption, difficulty, and/or proof-of-work schemes when mining blockchain data. Each encryption, difficulty, and/or proof-of-work scheme may be separate, stand-alone programs, files, or third-party services. Blockchain miners may be agnostic to a particular blockchain's encryption, difficulty, and/or proof-of-work schemes, thus allowing any blockchain miner to process or mine data in multiple blockchains. GPU, ASICs, and other specialized processing hardware components may be deterred by forcing cache misses, cache latencies, and processor stalls. Hashing, difficulty, and/or proof-of-work schemes require less programming code, consume less storage space/usage in bytes, and execute faster. Blockchain mining schemes may further randomize byte or memory block access, further improve cryptographic security.
The features, aspects, and advantages of the exemplary embodiments are understood when the following Detailed Description is read with reference to the accompanying drawings, wherein:
The exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings. The exemplary embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. These embodiments are provided so that this disclosure will be thorough and complete and will fully convey the exemplary embodiments to those of ordinary skill in the art. Moreover, all statements herein reciting embodiments, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure).
Thus, for example, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating the exemplary embodiments. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named manufacturer.
As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless expressly stated otherwise. It will be further understood that the terms “includes,” “comprises,” “including,” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being “connected” or “coupled” to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Furthermore, “connected” or “coupled” as used herein may include wirelessly connected or coupled. As used herein, the term “and/or” includes any and all combinations of one or more of the associated listed items.
It will also be understood that, although the terms first, second, etc. may be used herein to describe various elements, these elements should not be limited by these terms. These terms are only used to distinguish one element from another. For example, a first device could be termed a second device, and, similarly, a second device could be termed a first device without departing from the teachings of the disclosure.
The miner system 22 may mine the inputs 24. When the miner system 22 receives the inputs 24, the miner system 22 has a hardware processor (such as CPU 36) and a solid-state memory device 38 that collects the inputs 24 (such as the blockchain transactions 32) into a block 40 of data. The miner system 22 then finds a difficult proof-of-work (“PoW”) result 42 based on the block 40 of data. The miner system 22 performs, executes, or calls/requests a proof-of-work (“PoW”) mechanism 44. The proof-of-work mechanism 44 is a computer program, instruction(s), or code that instruct or cause the miner system 22 to call, request, and/or execute an encryption algorithm 46. The proof-of-work mechanism 44 may instruct or cause the miner system 22 to call, request, and/or execute a difficulty algorithm 48 that generates or creates a difficulty 50. The proof-of-work mechanism 44 may also instruct or cause the miner system 22 to call, request, and/or execute a proof-of-work (“PoW”) algorithm 52. The proof-of-work mechanism 44 may thus be one or more software applications or programming schemes that separate the encryption algorithm 46 from the difficulty algorithm 48 and/or from the proof-of-work algorithm 52. Because the encryption algorithm 46 may be separately executed/called from the difficulty algorithm 48 and/or from the proof-of-work algorithm 52, encryption of the electronic data 30 (representing the inputs 24) is separately performed from the difficulty 50 of solving the proof-of-work. In other words, any encryption algorithm 46 may be used, along with any difficulty algorithm 48, and/or along with any proof-of-work algorithm 52.
As
The miner system 22 may own the block 40 of data. If the miner system 22 is the first to satisfy the proof-of-work target scheme 34 (e.g., the proof-of-work result 42 satisfies the mathematical puzzle 62), the miner system 22 may timestamp the block 40 of data and broadcast the block 40 of data, the timestamp, the proof-of-work result 42, and/or the mathematical puzzle 62 to other miners in the blockchain environment 20. The miner system 22, for example, may broadcast a hash value representing the block 40 of data, and the other miners begin working on a next block in the blockchain 64.
Today's BITCOIN® difficulty is increasing. On or about Jun. 16, 2020, BITCOIN's network adjusted its difficulty level (the measure of how hard it is for miners to compete for block rewards on the blockchain) to 15.78 trillion, which was nearly a 15% increase in the difficulty 50. As the difficulty 50 increases, older, less capable, and less power efficient miners are unable to compete. As a result, today's BITCOIN® miners must have the latest, fastest hardware (such as an ASIC) to profitably solve the mathematical puzzle 62 according to the proof-of-work target scheme 34. Indeed, Satoshi envisioned that increasing hardware speed would allow miners to easier solve the proof-of-work, Satoshi thus explained that the difficulty would be a moving target to slow down generation of the blocks 40 of data.
Conventional mining schemes are integrated. When a conventional blockchain miner attempts to solve the mathematical puzzle 62, the conventional blockchain miner executes a conventional scheme that integrates hashing, difficulty, and proof-of-work. That is, conventional proof-of-work schemes require the miners to execute a combined software offering or pre-set combination of encryption and proof. These conventional proof-of-work scheme, in other words, integrate a predetermined encryption/hashing algorithm into or with a predetermined difficulty and a predetermined proof-of-work algorithm. These conventional proof-of-work schemes thus force the miners to execute a predetermined or predefined scheme that functionally marries or bundles encryption, difficulty, and proof-of-work.
The conventional schemes specify a difficulty mechanism. BITCOIN's difficulty mechanism, for example, is a measure of how difficult it is to mine a BITCOIN® block of data. BITCOIN® miners are required to find a hash value below a given target (e.g., SHA256(nonce+input) has n leading zeros, where n determines the mining difficulty). The difficulty adjustment is directly related to the total estimated mining power (sometimes estimated in Total Hash Rate per second). BITCOIN's difficulty mechanism is adjusted to basically ensure that ten (10) minutes of computation are required before a miner may solve the mathematical puzzle 62.
The conventional schemes force the use of specialized hardware. When blockchain mining first appeared, home/desktop computers and laptops (and their conventional processors or CPUs) were adequate. However, as blockchain mining became more difficult and competitive, miners gained an advantage by repurposing a dedicated graphics processing unit (or GPU) for blockchain mining. As an example, the RADEON® HD 5970 GPU has a clocked processing speed of executing about 3,200 of 32-bit instructions per clock, which is about 800 times more than the speed of a CPU that executes only four (4) 32-bit instructions per clock. This increased processor clock speed allowed GPUs to perform far more calculations and made GPUs more desirable for cryptocurrency/blockchain mining. Later, field programmable gate arrays (FPGAs) were also re-modeled for cryptocurrency/blockchain mining. FPGAs were able to compute the mathematical operations required to mine the block 40 of data twice as fast as the GPU. However, FPGA devices were more labor-intensive to build and still require customized configurations (both software programming and hardware). Today's BITCOIN® miners have pushed the hardware requirements even further by using a specialized application-specific integrated circuit (ASIC) that is exclusively designed for blockchain mining. These ASICs may be 100 billion times faster than mere CPUs. These ASICs have made BITCOIN® mining undemocratic and only possible by a relatively few, well capitalized entities running mining farms. Today's BITCOIN® miners thus consume great quantities of electrical power and pose concerns for the electrical grid.
Today's conventional mining hardware has further specialized. Some ASICs have also been further designed for particular blockchains to achieve additional optimizations. For example, a hardware implementation of the SHA-256 hash is much faster than a version coded in software. Today, nearly all BITCOIN® mining is performed using hardware ASICs. Specialized hardware has even been developed for particular hashing functions. The RAVENCOIN® scheme, as an example, uses several different hashing algorithms, and a particular hashing algorithm is picked for one block based off of a hash of a previous block (the RAVENCOIN® scheme resembles a random selection of the hashing algorithm). However, because fifteen (15) of the sixteen (16) algorithms sit on the sidelines unused at any given time, the RAVENCOIN® scheme makes it very expensive for a miner to buy sixteen (16) different hardware rigs in order to mine according to the RAVENCOIN® scheme. Even if a miner decides to only mine the blocks that match a particular hardware requirement, the hardware still sits idle 14-15 cycles on average.
Some blockchains may also alter or modify the mining scheme. For example, the MONERO® mining scheme uses a specialized hashing function that implements a random change. That is, the MONERO® mining scheme uses a hash algorithm that unpredictably rewrites itself. The MONERO® mining network introduced a RandomX mining algorithm that was designed to deter ASICs and to improve the efficiency of conventional CPUs. MONERO's RandomX mining algorithm uses random code execution and memory-intensive techniques, rendering ASICs too expensive and ineffective to develop.
The conventional mining schemes thus have many disadvantages. Conventional mining schemes have become so specialized and so expensive that only a small number of large miners have the resources to compete. Blockchain mining, in other words, has become centralized and undemocratic. Some conventional schemes try to find new hashing algorithms, new proof-of-work schemes, or modify existing schemes to de-centralize and to democratize mining participants. Some conventional mining schemes (such as ETHERIUM®) require very large memory spaces in bytes, which disadvantages its hardware. LITECOIN® also disadvantages hardware by copying large byte amounts of data.
As
Exemplary embodiments may thus use any encryption algorithm 46, any difficulty algorithm 48, and/or any proof-of-work algorithm 52. Exemplary embodiments may implement any cryptographic security. Instead of merely counting zeroes (as specified by BITCOIN®), exemplary embodiments may run the resulting hash value 60 through the difficulty algorithm 48 to calculate the difficulty 50 in order to determine whether it's more or less difficult than other hashes.
As
Exemplary embodiments may also use any difficulty scheme. The inventor envisions that there will be many different difficulty schemes. The difficulty algorithm 48, for example, may have to compare the difficulty 50 to a target difficulty 84. The target difficulty 84 has a bit or numeric value that represents a satisfactory difficulty of the corresponding cryptographic hashing algorithm 54 and/or the hash value 60. For example, suppose the target difficulty 84 is a minimum value that represents a minimum permissible difficulty associated with the corresponding cryptographic hashing algorithm 54. If the difficulty 50 is less than or perhaps equal to the target difficulty 84, then perhaps the corresponding cryptographic hashing algorithm 54 and/or the hash value 60 is adequately difficult. However, if the difficulty 50 is greater than the target difficulty 84, then perhaps the corresponding cryptographic hashing algorithm 54 and/or the hash value 60 is too difficult. Likewise, the difficulty 50 may need to be equal to or greater than the target difficulty 84 to be adequately difficult. Regardless, should the difficulty 50 fail to satisfy the target difficulty 84, exemplary embodiments may modify any data or input (e.g., the electronic data 30, a random number/nonce value, address, starting points, etc.) and recompute the corresponding hash value(s) 60. Moreover, exemplary embodiments may additionally or alternatively change the cryptographic hashing algorithm 54 and/or the difficulty algorithm 48 and recompute.
Exemplary embodiments may thus functionally separate hashing, difficulty, and proof-of-work. The conventional proof-of-work target scheme 34 functionally combines or performs both hashing and difficulty. The conventional proof-of-work target scheme 34 integrates or combines the difficulty in the hash. The conventional proof-of-work target scheme 34 integrates or combines the difficulty in the hash, thus greatly complicating the hash determination. Exemplary embodiments, instead, may separate the hashing algorithm 54 from the difficulty algorithm 48. Exemplary embodiments put the difficulty 50 in the measurement of the difficulty 50. Exemplary embodiments remove the difficulty 50 from the hashing algorithm 54. The hashing algorithm 54 is not complicated by also having to integrate/calculate the difficulty algorithm 48. The difficulty algorithm 48 may thus be a separate, stand-alone function or service that determines or calculates which hash is more difficult. The hashing algorithm 54 is much simpler to code and much faster to execute, as the hashing algorithm 54 requires less programming code and less storage space/usage in bytes. The hashing algorithm 54 need not be complicated to deter ASIC mining. Exemplary embodiments need not rely on the hashing algorithm 54 to also determine the difficulty 50 and/or the proof-of-work. The difficulty algorithm 48 is, instead, a separate functional mechanism, perhaps performed or executed by a service provider. Exemplary embodiments thus need not use an electrical power-hungry mechanism that is inherent in the conventional proof-of-work scheme.
Exemplary embodiments may thus force latency, cache misses, and stalls. Exemplary embodiments may target cache latency and processor stalls by generating, storing, and/or using the database table 90 when determining the hash value(s) 60 (as later paragraphs will explain). The database table 90, however, may be sized to overload the processor cache memory 100. The database table 90, in other words, may have a table byte size 102 (in bits/bytes) that exceeds a storage capacity or cache byte size 104 of the processor cache memory 100. The database table 90, for example, may exceed one gigabyte (1 GB). Today's L1, L2, and L3 processor cache memory is typically hundreds of megabits in size. Because the database table 90 may exceed one gigabyte (1 GB), any caching operation will miss or invalidate. That is, the L1, L2, and L3 processor cache memory 100 lacks the storage capacity or byte size 104 to store the entire database table 90. Perhaps only a portion (or perhaps none) of the database table 90 may be stored in the processor cache memory 100. Indeed, exemplary embodiments thus force some, most, or even all of the database table 90 to be written or stored to the main/host memory device 38 (or accessed/retrieved from a remote source, as later paragraphs will explain). Because any hardware processing element (again, whether a GPU, an ASIC, or the CPU 36) is unable to cache the entire database table 90, exemplary embodiments force a cache miss and further force the hardware processing element to repeatedly use the processor cache memory 100 to fetch and load a portion of the database table 90. The main/system memory 38 thus provides perhaps a particular portion of the database table 90 via the bus to the processor cache memory 100, and the processor cache memory 100 then provides that particular portion of the database table 90 to the hardware processing element. The hardware processing element may then purge or delete that particular portion of the database table 90 from the processor cache memory 100 and request/fetch/load another portion of the database table 90. Because exemplary embodiments may force repeated cache misses, the hardware processing element may continuously repeat this cycle for loading/retrieving most or all portions of the database table 90. The hardware processing element, in other words, repeatedly queries the processor cache memory 100 and/or the main/host memory device 38 and awaits data retrieval. The hardware processing element must, therefore sit, perhaps mostly idle, while the processor cache memory 100 and/or the main/host memory device 38 processes, retrieves, and sends different segments/portions/blocks of the database table 90. The processor cache memory 100 and/or the main/host memory device 38 have the cache latency (perhaps measured in clock cycles, data transfer rate, or time) that limits blockchain computations. A faster processor/GPU/ASIC, in other words, will not improve memory access times/speeds, so any computational speed/performance is limited by the latency of repeatedly accessing the processor cache memory 100 and/or the main/host memory device 38. The database table 90 thus deters GPU/ASIC usage when processing the blockchain transactions 32. The database table 90 may thus be purposefully designed to be non-cacheable by intensively using the processor cache memory 100 and/or the main/host memory device 38 as an ASIC-deterrence mechanism.
Byte or memory block access may be randomized. Whatever the hashing algorithm 54, exemplary embodiments may implement the bit shuffle operation 92 on the hash value(s) 60. Exemplary embodiments may use the entries 94 in the database table 90 to perform the bit shuffle operation 92 (as later paragraphs will further explain). The proof-of-work target scheme 34 may use bit values representing the hash value(s) 60, but the proof-of-work target scheme 34 may swap any one or more of the bit values with any one or more entries 94 specified by the database table 90. Each entry 94 in the database table 90 may contain a random selection of bits/bytes. The proof-of-work target scheme 34 may cause the proof-of-work algorithm 52 to read or to select a bit portion of the bit values representing the hash value(s) 60 and exchange or replace the bit portion with an entry 94 contained in, or referenced by, the database table 90. Each entry 94 in the database table 90 represents or is associated with random bits or bytes. The proof-of-work target scheme 34 may thus randomly shuffle the hash value(s) 60 generated by the cryptographic hashing algorithm 54.
Exemplary embodiments may discourage or deter specialized hardware in blockchain mining. The miner system 22 must have access to the database table 90 in order to execute the bit shuffle operation 92, difficulty algorithm 48, and/or the proof-of-work algorithm 52. Because any processing component (e.g., ASIC, GPU, or the CPU 36) is unable to cache the entire database table 90, exemplary embodiments force the processing component to query the processor cache memory 100 and/or the main/host memory device 38 and to await data retrieval. The hardware processing component must therefore sit, perhaps mostly idle, while the processor cache memory 100 and/or the main/host memory device 38 processes, retrieves, and sends different segments/portions/blocks of the database table 90. A faster GPU/ASIC will thus not improve memory access times/speeds. Exemplary embodiments thus force miners to choose the CPU 36, as a faster GPU/ASIC provides no performance/speed gain. Moreover, because a faster GPU/ASIC is ineffective, the extra capital expense of a faster GPU/ASIC offers little or no benefit and cannot be justified. Exemplary embodiments thus bind miners to the CPU 36 for blockchain processing/mining.
Exemplary embodiments thus include RAM hashing. The electronic database table 90 may have a random number of columns and/or a random number of rows. The electronic database table 90 may have a random number of database entries 94. Moreover, each columnar/row database entry 94 may also have a random sequence or selection of bits/bytes (1's and 0's). So, whatever the hash values 60 generated by the hashing algorithm 54, the separate difficulty algorithm 48 and/or proof-of-work algorithm 52 may use the electronic database table 90 to further randomize the hash values 60 for additional cryptographic security. Indeed, because only at least a portion of the electronic database table 90 may be stored in the processor cache memory 100, exemplary embodiments effectively confine hashing operations to the main/host memory device 38 (such as a subsystem RAM). Regardless of what device or service provider executes the hashing algorithm 54, the electronic database table 90, which is mostly or entirely stored in the main/host memory device 38, provides the randomized inputs to the separate difficulty algorithm 48 and/or proof-of-work algorithm 52. Operationally and functionally, then, exemplary embodiments divorce or functionally separate any hardware processing element from the hashing operation. Simply put, no matter what the performance/speed/capability of the ASIC, GPU, or the CPU 36, the database table 90 may be randomly sized to always exceed the storage capacity or cache byte size 104 of the processor cache memory 100. Hashing operations are thus reliant on cache latency, cache misses, and processor stalk when using the database table 90. The hashing operations are thus largely confined to, and performed by, the off-board or off-processor main/host memory device 38 (such as a subsystem RAM). Because the main host memory device 38 performs most or all of the cryptographic security, the hardware processing component (ASIC, GPU, or the CPU 36) may play little or no role in the hashing operations (perhaps only performing database lookup queries). Again, a better/faster ASIC or GPU provides little to no advantage in the hashing operations. Moreover, the main/host memory device 38 consumes much less electrical power, thus further providing reduced energy costs that deter/resist ASIC/GPU usage.
Exemplary embodiments may also add cryptographic security. Exemplary embodiments may force the miner/network to possess, or have authorized access to, the database table 90. In simple words, the proof-of-work target scheme 34 swaps random bytes in the hash value 60 with other random bytes specified by the database table 90. Any party that provides or determines a proof-of-work must possess (or have access to) the database table 90. If the difficulty algorithm 48 and/or the proof-of-work algorithm 52 lacks authorized access to the database table 90, then the difficulty algorithm 48 and/or the proof-of-work algorithm 52 cannot query the database table 90 nor perform database lookup operations. Difficulty and/or proof-of-work will fail without having access to the database table 90.
Exemplary embodiments may also separately specify the difficulty algorithm 48. The proof-of-work target scheme 34 may cause the miner system 22 to apply the bit shuffle operation 92 to the hash value 60. The proof-of-work target scheme 34 may also specify the difficulty algorithm 48 and the target difficulty 84, perhaps having a high number or value. Because these byte accesses to the processor cache memory 100 are random and over a gigabyte of the memory space, the byte accesses blow or exceed the retrieval and/or byte size storage capabilities of the processor cache memory 100. The proof-of-work target scheme 34 thus forces the miner system 22 to wait on the slower main/host memory device 38 (rather than waiting on the speed of the hardware processing component). A faster/better hardware processing element (such as an ASIC), in other words, does not alleviate the bottleneck of accessing the main/host memory device 38. Moreover, because exemplary embodiments may heavily rely on the main/host memory device 38 (rather than the hardware processing component) to do proof of work, the miner system 22 consumes significantly less of electrical power (supplied by a power supply 110). Because the proof-of-work algorithm 52 and the difficulty algorithm 48 may be separate from the cryptographic hashing algorithm 54, exemplary embodiments utilize the security of a well-tested hashing function, but exemplary embodiments also require the proof-of-work scheme to use the main/host memory device 38, which makes it unreasonable to build ASICS.
Exemplary embodiments may thus force usage of a particular physical memory. Exemplary embodiments, for example, may overload the processor cache memory 100 by gorging the byte size of the database table 90 with additional database entries. Even as L1, L2, and L3 processor cache memory 100 increases in the storage capacity or byte size 104, exemplary embodiments may concomitantly increase the table byte size 102 (in bits/bytes) to ensure the database table 90 continues to exceeds the storage capacity or byte size 104 of the processor cache memory 100. Exemplary embodiments may thus bind the encryption algorithm 46, the difficulty algorithm 48, and/or the proof-of-work algorithm 52 to the main/host memory device 38 to deter GPU/ASIC usage.
Exemplary embodiments may also unbind the hashing algorithm 54 from the difficulty algorithm 48. Exemplary embodiments easily validate the proof-of-work by changing how proof-of-work is calculated without changing the hashing algorithm 54. Because the hashing algorithm 54 is disassociated or disconnected from the difficulty algorithm 48, the cryptographically security of the hashing algorithm 54 is increased or improved. Moreover, the separate difficulty algorithm 48 and/or proof-of-work algorithm 52 may have other/different objectives, without compromising the cryptographically security of the hashing algorithm 54. The difficulty algorithm 48 and/or proof-of-work algorithm 52, for example, may be designed for less consumption of the electrical power. The difficulty algorithm 48 and/or proof-of-work algorithm 52 may additionally or alternatively be designed to deter/resist ASIC/GPU usage, such as increased usage of the processor cache memory 100 and/or the main/host memory device 38. The difficulty algorithm 48 and/or proof-of-work algorithm 52 need not be cryptographically secure. Because the hashing algorithm 54 ensures the cryptographically security, the difficulty algorithm 48 and/or proof-of-work algorithm 52 need not be burdened with providing the cryptographically security. The difficulty algorithm 48 and/or proof-of-work algorithm 52 each require less programming code and less storage space/usage in bytes, so each is much simpler to code and much faster to execute.
Exemplary embodiments may use a network latency 112 to discourage or deter specialized hardware. Because the blockchain network server 28 may store the database table 90, the miner system 22 is performance bound by the network latency 112 in the communications network 26. Packet communications between the blockchain network server 28 and the destination miner system 22 require time, and the network latency 112 is affected by network routing, network segment travel distances, network traffic, and many other factors. Exemplary embodiments may thus additionally or alternatively force the miner system 22 to wait on the communications network 26 to obtain any entry 94 in the database table 90. A faster/better hardware processing component (such as an ASIC) does not overcome bottleneck(s) due to the network latency 112 in the communications network 26. Moreover, because the electrical power required by a network interface 114 is likely less than the hardware processing component, the miner system 22 consumes significantly less of electrical power.
Exemplary embodiments may thus be socially bound. Because the party identifier 126 may be an input to the difficulty algorithm 48 and/or to the proof-of-work algorithm 52, the party identifier 126 must specify the correct name, code, alphanumeric combination, binary value, or any other representation of the PoW service provider 120. If the wrong, incorrect, or unauthorized value is input, the difficulty algorithm 48 and/or the proof-of-work algorithm 52 will generate incorrect results that cannot satisfy the proof-of-work (“PoW”) target scheme 34. An unauthorized party has been used to conduct the proof-of-work.
Exemplary embodiments may thus decouple encryption, difficulty, and proof-of-work efforts. Because the encryption algorithm 46 may be a stand-alone software offering or module, exemplary embodiments greatly improve encryption security. The encryption algorithm 46 (such as the hashing algorithm 54) need not intertwine with the difficulty algorithm 48 and/or the proof-of-work algorithm 52. Because the hashing algorithm 54 may be functionally divorced from difficulty and proof-of-work calculations, the hashing algorithm 54 remains a safe, secure, and proven cryptology scheme without exposure to software bugs and errors introduced by difficulty and proof-of-work needs. The difficulty algorithm 48 may also be severed or isolated from encryption and proof-of-work, thus allowing a blockchain scheme to dynamically alter or vary different difficulty calculations without affecting encryption and/or proof-of-work. The proof-of-work algorithm 52 may also be partitioned, split off, or disconnected from encryption and difficulty, thus allowing any blockchain scheme to dynamically alter or vary different proof-of-work calculations or schemes without affecting encryption and/or difficulty.
Exemplary embodiments reduce energy consumption. Because a conventional, general purpose central processing unit (e.g., the CPU 36) is adequate for mining the blockchain transactions 32, exemplary embodiments consume much less electrical power. Moreover, because a conventional central processing unit consumes much less electrical power, the CPU operates at much cooler temperatures, generates less waste heat/energy, and therefore requires less cooling, air conditioning, and refrigerant machinery. Exemplary embodiments are thus much cheaper to operate than GPUs and ASICs.
Exemplary embodiments thus democratize blockchain mining. Because encryption, difficulty, and proof-of-work efforts may be functionally divided, general-purpose computer equipment has the processing and memory capability to compete as blockchain miners. For example, because the function(s) that calculate(s) the magnitude of the proof of work (such as the difficulty algorithm 48 and/or the proof-of-work algorithm 52) may be detached or isolated from the function that performs cryptography (such as the hashing algorithm 54), encryption need not be modified in order to improve security (e.g., such as the MONERO® mining scheme). The well-tested SHA-256 hashing function, for example, remains stable and unaffected by difficulty and/or proof-of-work. The difficulty algorithm 48, in other words, need not be determined by or with the hashing algorithm 54. The difficulty algorithm 48, instead, may be separately determined as a true, independent measure of the difficulty 50. The inventor has realized that most or all proof of work schemes generally may have two functions (i.e., one function to do a cryptographic hash and another function to determine the level of difficulty of a given hash). Exemplary embodiments may separate, or take away, what makes proof of work hard from the cryptographic hash and, perhaps instead, put it in the difficulty algorithm 48 that calculates which hash is more difficult. The difficulty algorithm 48, for example, may be functionally combined with the proof-of-work algorithm 52 that calculates the magnitude of the proof of work instead of using the hashing algorithm 54 (as
Encryption may thus be independent from proof-of-work determinations. The proof of work (such as the difficulty algorithm 48 and/or the proof-of-work algorithm 52) may be a different or separate software mechanism from the hashing mechanism. The difficulty 50 of the proof-of-work, for example, may be a separate component from staking in a blockchain. The difficulty algorithm 48 and/or the proof-of-work algorithm 52 may require communications networking between provably different parties. The difficulty algorithm 48 and/or the proof-of-work algorithm 52 may require network delays and/or memory bandwidth limitations. The difficulty algorithm 48 and/or the proof-of-work algorithm 52 may have a random component (such as incorporating a random function), such that the difficulty algorithm 48 and/or the proof-of-work algorithm 52 may randomly to determine the difficulty 50 and/or the proof-of-work result 42. Exemplary embodiments thus reduce or even eliminate the power intensive mechanism that is inherent in today's proof of work schemes by changing how the proof of work is calculated. Exemplary embodiments need not change the hashing algorithm 54, and exemplary embodiments allow a more easily validated proof of work. The hashing algorithm 54 is not bound or required to determine the proof of work. The proof of work need not be cryptographically secure. The liberated, autonomous hashing algorithm 54 generates and guarantees an input (e.g., the hash values 60) that cannot be predicted by some other faster algorithm. The disassociated hashing algorithm 54 effectively generates the hash values 60 as random numbers. The hashing algorithm 54, in other words, provides cryptographic security, so neither the difficulty algorithm 48 nor the proof-of-work algorithm 52 need be cryptographically secure. The difficulty algorithm 48 and/or the proof-of-work algorithm 52 need not be folded into the hashing algorithm 54.
Exemplary embodiments provide great value to blockchains. Exemplary embodiments may functionally separate encryption (e.g., the hashing algorithm 54) from proof of work (such as the difficulty algorithm 48 and/or the proof-of-work algorithm 52). Exemplary embodiments may thus hind proof-of-work to a conventional central processing unit. Deploying a different cryptographic hash is hugely dangerous for blockchains, but deploying another difficulty or proof of work mechanism is not so dangerous. Exemplary embodiments allow blockchains to experiment with different difficulty functions (the difficulty algorithms 48) and/or different proof-of-work algorithms 52 without changing the hashing algorithm 54. Exemplary embodiments thus mitigate risk and reduce problems with cryptographic security. Many blockchain environments would prefer to make their technology CPU mineable for lower power, lower costs, and more democratic participation. The barrier, though, is that conventionally these goals would require changing their hash function. Exemplary embodiments, instead, reduce costs and increase the pool of miner systems without changing the hash function. The difficulty algorithm 48 and/or the proof-of-work algorithm 52 may be refined, modified, or even replaced with little or no impact on the hashing algorithm 54.
Exemplary embodiments reduce electrical power consumption. Blockchain mining is very competitive, as the first miner that solves the mathematical puzzle 62 owns the block 40 of data and is financially rewarded. Large “farms” have thus overtaken blockchain mining, with each miner installation using hundreds or even thousands of ASIC-based computers to improve their chances of first solving the calculations specified by the mathematical puzzle 62. ASIC-based blockchain mining requires tremendous energy resources, though, with some studies estimating that each BITCOIN® transaction consumes more daily electricity than an average American home. Moreover, because ASIC-based blockchain mining operates 24/7/365 at full processing power, the ASIC-based machines quickly wear out or fail and need periodic (perhaps yearly) replacement. Exemplary embodiments, instead, retarget blockchain mining back to CPU-based machines that consume far less electrical power and that cost far less money to purchase. Because the capital costs and expenses are greatly reduced, more miners and more CPU-based machines may effectively participate and compete. The CPU-based machines, in other words, have a realistic and profitable chance of first solving the calculations specified by the mathematical puzzle 62. Democratic participation is greatly increased.
The miner system 22 operates as a mining node in the blockchain environment 20. The miner system 22 has the central processing unit (e.g., “CPU”) 36 that executes a client-side blockchain mining software application 196 stored in the local memory device 38. The miner system 22 has a network interface to the communications network 26, thus allowing two-way, bidirectional communication with the blockchain network server 28. The client-side blockchain mining software application 196 includes instructions, code, and/or programs that cause the miner system 22 to perform operations, such as receiving the inputs 24, the electronic data 30, and/or the proof-of-work (“PoW”) target scheme 34. The client-side blockchain mining software application 196 may then cause the miner system 22 to execute the proof-of-work (“PoW”) mechanism 44 based on the electronic data 30 representing the inputs 24. The client-side blockchain mining software application 196 may instruct the CPU 36 to call and/or to execute the encryption algorithm 46, the difficulty algorithm 48, and/or the PoW algorithm 52. The CPU 36 calls or executes any or all of the encryption algorithm 46, the difficulty algorithm 48, and/or the PoW algorithm 52 using the electronic data 30.
The miner system 22 mines blockchain transactional records. Whatever the electronic data 30 represents, the miner system 22 applies the electronic data 30 according to the proof-of-work target scheme 34. While the proof-of-work target scheme 34 may specify any encryption algorithm 46, most blockchains specify the hashing algorithm 54. The miner system 22 may thus generate the hash values 60 by hashing the electronic data 30 (e.g., the blockchain transactions 32) using the hashing algorithm 54. The miner system 22 may generate the difficulty 50 by executing the difficulty algorithm 48 using the hash values 60. The miner system 22 may generate the proof-of-work result 42 using the hash value(s) 60 as inputs to the proof-of-work algorithm 52. If the proof-of-work result 42 satisfies the mathematical puzzle 62, according to the rules/regulations specified by the blockchain network server 28 and/or the proof-of-work target scheme 34, then perhaps the miner system 22 earns or owns the right or ability to write/record blockchain transaction(s) to the block 40 of data. The miner system 22 may also earn or be rewarded with a compensation (such as a cryptographic coin, points, other currency/coin/money, or other value).
The miner system 22 may own the block 40 of data. If the miner system 22 is the first to satisfy the proof-of-work target scheme 34 (e.g., the proof-of-work result 42 satisfies the mathematical puzzle 62), the miner system 22 earns the sole right or ability to write the blockchain transactions 32 to the block 40 of data. The miner system 22 may timestamp the block 40 of data and broadcast the block 40 of data, the timestamp, the proof-of-work result 42, and/or the mathematical puzzle 62 to other miners in the blockchain environment 20. The miner system 22, may broadcast a hash value representing the block 40 of data. The miner system 22 thus adds or chains the block 40 of data (and perhaps its hash value) to the blockchain 64, and the other miners begin working on a next block in the blockchain 64.
The proof-of-work target scheme 34 and/or the mathematical puzzle 62 may vary. Satoshi's BITCOIN® proof-of-work scanned for a value that, when hashed, the hash value begins with a number of zero bits. The average work required is exponential in the number of zero bits required and can be verified by executing a single hash. BITCOIN's miners may increment a nonce in the block 40 of data until a value is found that gives the block's hash the required zero bits.
Other suppliers may be used. The difficulty server 160 may communicate with the blockchain network server 28 and the miner system 22 via the communications network 26. The difficulty server 160 has a hardware processing element (“P”) that executes the difficulty algorithm 48 stored in a local memory device. The difficulty service provider 156 may provide the difficulty service 158 by instructing the difficulty server 160 to execute the difficulty algorithm 48 chosen or specified by the miner system 22 and/or the blockchain network server 28. The miner system 22 and/or the blockchain network server 28 may send a difficulty service request to the difficulty server 160, and the difficulty service request may specify the hash value(s) 60. The difficulty server 160 executes the difficulty algorithm 48 using the hash value(s) 60 to generate the difficulty 50. The difficulty server 160 sends the service response to the miner system 22, and the service response includes or specifies the difficulty 50. The PoW server 124 may communicate with the blockchain network server 28 and the miner system 22 via the communications network 26. The PoW server 124 has a hardware processing element (“P”) that executes the proof-of-work algorithm 52 stored in a local memory device. The PoW service provider 120 (e.g., the POW server 124) may provide the PoW service 122 by executing the proof-of-work algorithm 52 chosen or specified by the miner system 22 and/or the blockchain network server 28. The POW server 124 sends the service response to the miner system 22, and the service response includes or specifies the PoW result 42. The miner system 22 may compare any of the hash value(s) 60, the difficulty 50, and/or the PoW result 42 to the proof-of-work target scheme 34. If the proof-of-work target scheme 34 is satisfied, perhaps the miner system 22 is the first miner to have solved the puzzle 62.
Exemplary embodiments may be applied regardless of networking environment. Exemplary embodiments may be easily adapted to stationary or mobile devices having wide-area networking (e.g., 4G/LTE/5G cellular), wireless local area networking (WI-FI®), near field, and/or BLUETOOTH® capability. Exemplary embodiments may be applied to stationary or mobile devices utilizing any portion of the electromagnetic spectrum and any signaling standard (such as the IEEE 802 family of standards, GSM/CDMA/TDMA or any cellular standard, and/or the ISM band). Exemplary embodiments, however, may be applied to any processor-controlled device operating in the radio-frequency domain and/or the Internet Protocol (IP) domain. Exemplary embodiments may be applied to any processor-controlled device utilizing a distributed computing network, such as the Internet (sometimes alternatively known as the “World Wide Web”), an intranet, a local-area network (LAN), and/or a wide-area network (WAN). Exemplary embodiments may be applied to any processor-controlled device utilizing power line technologies, in which signals are communicated via electrical wiring. Indeed, exemplary embodiments may be applied regardless of physical componentry, physical configuration, or communications standard(s).
Exemplary embodiments may utilize any processing component, configuration, or system. For example, the miner system 22 may utilize any desktop, mobile, or server central processing unit or chipset offered by INTEL®, ADVANCED MICRO DEVICES®, ARM®, TAIWAN SEMICONDUCTOR MANUFACTURING®, QUALCOMM®, or any other manufacturer. The miner system 22 may even use multiple central processing units or chipsets, which could include distributed processors or parallel processors in a single machine or multiple machines. The central processing unit or chipset can be used in supporting a virtual processing environment. The central processing unit or chipset could include a state machine or logic controller. When any of the central processing units or chipsets execute instructions to perform “operations,” this could include the central processing unit or chipset performing the operations directly and/or facilitating, directing, or cooperating with another device or component to perform the operations.
Exemplary embodiments may packetize. When the blockchain network server 28 and the miner system 22 communicate via the communications network 26, the blockchain network server 28 and the miner system 22 may collect, send, and retrieve information. The information may be formatted or generated as packets of data according to a packet protocol (such as the Internet Protocol). The packets of data contain bits or bytes of data describing the contents, or payload, of a message. A header of each packet of data may be read or inspected and contain routing information identifying an origination address and/or a destination address.
Exemplary embodiments may use any encryption or hashing function. There are many encryption algorithms and schemes, and exemplary embodiments may be adapted to execute or to conform to any encryption algorithm and/or scheme, in the blockchain environment 20, though, many readers may be familiar with the various hashing algorithms, especially the well-known SHA-256 hashing algorithm. The SHA-256 hashing algorithm acts on any electronic data or information to generate a 256-bit hash value as a cryptographic key. The key is thus a unique digital signature. However, there are many different hashing algorithms, and exemplary embodiments may be adapted to execute or to conform to any hashing algorithm, hashing family, and/or hashing scheme (e.g., Blake family, MD family, RIPE family, SHA family, CRC family).
The miner system 22 may store or request different software packages. The hashing algorithm 54 may be a software file, executable program, routine, module, programming code, or third-party service that hashes the blockchain transactions 32 to generate the hash value(s) 60. The difficulty algorithm 48 may be a software file, executable program, routine, module, programming code, or third-party service that uses the hash value(s) 60 to generate the difficulty 50. The proof-of-work (“PoW”) algorithm 52 be a software file, executable program, routine, module, programming code, or third-party service that uses the hash value(s) 60 to generate the PoW result 42. The miner system 22 may download or otherwise acquire the hashing algorithm 54, the difficulty algorithm 48, and/or the PoW algorithm 52 to provide mining operations for the blockchain transactions 32.
The blockchain environment 20 may flexibly switch or interchange encryption, difficulty, and proof-of-work. Because the hashing algorithm 54, the difficulty algorithm 48, and the proof-of-work algorithm 52 may be separate software packages, the proof-of-work (“PoW”) target scheme 34 and/or the blockchain environment 20 may mix-and-match the encryption algorithm 46, the difficulty algorithm 48, and the proof-of-work algorithm 52. The blockchain environment 20 may thus easily evaluate different combinations of the encryption algorithm 46, the difficulty algorithm 48, and the proof-of-work algorithm 52 with little or no intra-algorithm or intra-application effect. The blockchain environment 20 may mix-and-match encryption, difficulty, and proof-of-work.
As
Exemplary embodiments may outsource encryption operations. When the miner system 22 determines the encryption identifier 200, the corresponding blockchain encryption scheme may require or specify the encryption service provider 150 that provides the encryption service 152. As
Exemplary embodiments may thus be agnostic to hashing. The miner system 22 may call, request, and/or execute any encryption scheme specified by any client, cryptographic coin, or blockchain network. The miner system 22 may dynamically switch or mix-and-match different encryption schemes. Once the miner system 22 determines the proof-of-work target scheme 34, the encryption algorithm 46, the encryption service provider 150, the encryption service 152, the encryption identifier 200, and/or the encryption service resource 204, the miner system 22 may perform any encryption scheme specified for the blockchain environment 20. The blockchain environment 20 may dynamically change the encryption scheme at any time. The blockchain environment 20 may flexibly switch, change, and evaluate different encryption strategies, perhaps with little or no impact or effect on difficulty and proof-of-work operations. Moreover, the miner system 22 may operate within or mine different blockchain environments 20 without specialized hardware rigs.
Exemplary embodiments improve computer functioning. Because exemplary embodiments may only specify the encryption identifier 200, the memory byte size consumed by the proof-of-work (“PoW”) target scheme 34 and/or the client-side blockchain mining software application 196 is reduced. That is, the blockchain network server 28 need not send the entire software program, code, or instructions representing the hashing algorithm 54 used by the blockchain environment 20. The blockchain environment 20, the blockchain network server 28, and/or the proof-of-work (“PoW”) target scheme 34 need only specify much smaller byte-sized data or information representing the encryption algorithm 46, the encryption service provider 150, the encryption service 152, the encryption identifier 200, and/or the encryption service resource 204. The blockchain environment 20 need not be burdened with conveying the hashing algorithm 54 to the miner system 22 and other mining nodes. The blockchain environment 20 and the communications network 26 convey less packet traffic, so packet travel times and network latency are reduced. Moreover, especially if the miner system 22 outsources the hashing operation, the miner system 22 is relieved from processing/executing the hashing algorithm 54 and consumes less of the electrical power. Again, then, a faster and more expensive graphics processor or even ASIC will not speed up the hashing operation. The conventional central processing unit 36 is adequate, reduces costs, and promotes democratic mining.
As
Exemplary embodiments may outsource difficulty operations. When the miner system 22 determines the difficulty identifier 210, the corresponding blockchain difficulty scheme may require or specify the difficulty service provider 156 that provides the difficulty service 158. As
Exemplary embodiments may thus be agnostic to difficulty. The miner system 22 may call, request, and/or execute any difficulty scheme specified by any client, cryptographic coin, or blockchain network. The miner system 22 may dynamically switch or mix-and-match different difficulty schemes. Once the miner system 22 determines the proof-of-work target scheme 34, the difficulty algorithm 48, the difficulty service provider 156, the difficulty service 158, the difficulty identifier 210, and/or the difficulty service resource 212, the miner system 22 may perform any difficulty scheme specified for the blockchain environment 20. The blockchain environment 20 may dynamically change the difficulty scheme at any time. The blockchain environment 20 may flexibly switch, change, and evaluate different difficulty strategies, perhaps with little or no impact or effect on hashing and proof-of-work operations. Moreover, the miner system 22 may operate within or mine different blockchain environments 20 without specialized hardware rigs.
Exemplary embodiments improve computer functioning. Because exemplary embodiments may only specify the difficulty identifier 210, the memory byte size consumed by the proof-of-work (“PoW”) target scheme 34 and/or the client-side blockchain mining software application 196 is reduced. That is, the blockchain network server 28 need not send the entire software program, code, or instructions representing the difficulty algorithm 48 used by the blockchain environment 20. The blockchain environment 20, the blockchain network server 28, and/or the proof-of-work (“PoW”) target scheme 34 need only specify much smaller byte-sized data or information representing the difficulty algorithm 48, the difficulty service provider 156, the difficulty service 158, the difficulty identifier 210, and/or the difficulty service resource 212. The blockchain environment 20 need not be burdened with conveying the difficulty algorithm 48 to the miner system 22 and other mining nodes. The blockchain environment 20 and the communications network 26 convey less packet traffic, so packet travel times and network latency are reduced. Moreover, especially if the miner system 22 outsources the difficulty operation, the miner system 22 is relieved from processing/executing the difficulty algorithm 48 and consumes less of the electrical power. Again, then, a faster and more expensive graphics processor or even ASIC will not speed up the difficulty operation. The conventional central processing unit 36 is adequate, reduces costs, and promotes democratic mining.
As
Exemplary embodiments may outsource difficulty operations. When the miner system 22 determines the PoW identifier 214, the corresponding blockchain proof-of-work scheme may require or specify the PoW service provider 120 that provides the PoW service 122. As
Exemplary embodiments may thus be agnostic to proof-of-work. The miner system 22 may call, request, and/or execute any proof-of-work scheme specified by any client, cryptographic coin, or blockchain network. The miner system 22 may dynamically switch or mix-and-match different proof-of-work schemes. Once the miner system 22 determines the proof-of-work target scheme 34, the PoW algorithm 52, the PoW service provider 120, the PoW service 122, the PoW identifier 214, and/or the PoW service resource 216, the miner system 22 may perform any proof-of-work scheme specified for the blockchain environment 20. The blockchain environment 20 may dynamically change the proof-of-work scheme at any time. The blockchain environment 20 may flexibly switch, change, and evaluate different proof-of-work strategies, perhaps with little or no impact or effect on hashing and difficulty operations. Moreover, the miner system 22 may operate within or mine different blockchain environments 20 without specialized hardware rigs.
Exemplary embodiments improve computer functioning. Because exemplary embodiments may only specify the PoW identifier 214, the memory byte size consumed by the proof-of-work (“PoW”) target scheme 34 and/or the client-side blockchain mining software application 196 is reduced. That is, the blockchain network server 28 need not send the entire software program, code, or instructions representing the PoW algorithm 52 used by the blockchain environment 20. The blockchain environment 20, the blockchain network server 28, and/or the proof-of-work (“PoW”) target scheme 34 need only specify much smaller byte-sized data or information representing the PoW algorithm 52, the PoW service provider 120, the PoW service 122, the PoW identifier 214, and/or the PoW service resource 216. The blockchain environment 20 need not be burdened with conveying the PoW algorithm 52 to the miner system 22 and other mining nodes. The blockchain environment 20 and the communications network 26 convey less packet traffic, so packet travel times and network latency are reduced. Moreover, especially if the miner system 22 outsources the proof-of-work operation, the miner system 22 is relieved from processing/executing the PoW algorithm 52 and consumes less of the electrical power. Again, then, a faster and more expensive graphics processor or even ASIC will not speed up the difficulty operation. The conventional central processing unit 36 is adequate, reduces costs, and promotes democratic mining.
The miner system 22 may remotely retrieve the difficulty algorithm 48. After the miner system 22 determines the proof-of-work (“PoW”) target scheme 34 that is required by the blockchain environment 20, the miner system 22 may acquire or download the difficulty algorithm 48. For example, the miner system 22 may determine the difficulty identifier 210 (as this disclosure above explains) and send a query to the difficulty server 160. The query specifies the difficulty identifier 210. When the difficulty server 160 receives the query, the difficulty server 160 may query the database 74 of difficulty algorithms for the difficulty identifier 210. The difficulty server 160 may locally store the database 74 of difficulty algorithms and function as a networked difficulty resource for clients. The difficulty server 160 identifies and/or retrieves the corresponding difficulty algorithm 48. The difficulty server 160 sends a query response to the miner system 22, and the query response specifies or includes the corresponding difficulty algorithm 48. The miner system 22 may then execute the difficulty algorithm 48, as above explained.
The miner system 22 may remotely retrieve the PoW algorithm 52. After the miner system 22 determines the proof-of-work (“PoW”) target scheme 34 that is required by the blockchain environment 20, the miner system 22 may acquire or download the PoW algorithm 52. For example, the miner system 22 may determine the PoW identifier 214 (as this disclosure above explains) and send a query to the PoW server 124. The query specifies the PoW identifier 214. When the PoW server 124 receives the query, the PoW server 124 may query the database 78 of PoW algorithms for the PoW identifier 214. The PoW server 124 may locally store the database 78 of PoW algorithms and function as a networked proof-of-work resource for clients. The PoW server 124 identifies and/or retrieves the corresponding PoW algorithm 52. The PoW server 124 sends a query response to the miner system 22, and the query response specifies or includes the corresponding PoW algorithm 52. The miner system 22 may then execute the PoW algorithm 52, as above explained.
Exemplary embodiments may auto-size the database table 90. When the client-side blockchain mining application 196 determines the storage capacity or cache byte size 104 of the processor cache memory 100, the client-side blockchain mining application 196 may compare the storage capacity or cache byte size 104 to the table byte size 102 of the database table 90. The storage capacity or cache byte size 104 of the processor cache memory 100, for example, may be subtracted from the table byte size 102 of the database table 90. If the resulting value (in bits/bytes) is positive (greater than zero), then the database table 90 exceeds the storage capacity or cache byte size 104 of the processor cache memory 100. The client-side blockchain mining application 196 may thus determine a cache deficit 236, ensuring the cache miss 232 and the cache latency 234.
Exemplary embodiments, however, may determine a cache surplus 238. If the resulting value (in bits/bytes) is zero or negative, then the database table 90 is less than the storage capacity or cache byte size 104 of the processor cache memory 100. Whatever the processing component (whether a GPU, ASIC, or the CPU 36), some or even all of the database table 90 could be stored and retrieved from the processor cache memory 100, thus giving an advantage to a faster processing component. The client-side blockchain mining application 196 may thus increase the table byte size 102 of the database table 90. The client-side blockchain mining application 196, for example, may add one (1) or more additional database rows 240 and/or one (1) or more additional database columns 242. The client-side blockchain mining application 196 may increase the table byte size 102 of the database table 90 by adding additional entries 94, with each added entry 94 specifying more random bits 96. As an example, the client-side blockchain mining application 196 may call, use, or execute the random number generator 222 to generate the random number 224 and then add the additional database row(s) 240 and/or additional database column(s) 242 according to the random number 224. Exemplary embodiments may thus continually or periodically monitor the storage capacity or cache byte size 104 of the processor cache memory 100 and the table byte size 102 of the database table 90. The cache surplus 238 may trigger a resizing operation to ensure the database table 90 always exceeds the processor cache memory 100.
The database table 90 may be large. The above examples only illustrated a simple configuration of a few database entries 94. In actual practice, though, the database table 90 may have hundreds, thousands, or even millions of the rows and columns, perhaps producing hundreds, thousands, millions, or even billions of database entries 94. Exemplary embodiments may repeatedly perform the bit shuffle operation 92 to suit any difficulty or proof-of-work strategy or scheme. The proof-of-work target scheme 34, the difficulty algorithm 48, and/or the proof-of-work algorithm 52 may each specify a minimum and/or a maximum number of bit shuffle operations that are performed.
Exemplary embodiments may use the XOR/Shift random number generator (RNG) 222 coupled with the lookup database table 90 of randomized sets of bytes. The database table 90 may have any number of 256 byte tables combined and shuffled into one large byte lookup table. Exemplary embodiments may then index into this large table to translate the state built up while hashing into deterministic but random byte values. Using a 1 GB lookup table results in a RAM Hash PoW algorithm that spends over 90% of its execution time waiting on memory (RAM) than it does computing the hash. This means far less power consumption, and ASIC and GPU resistance. The ideal platform for PoW using a RAM Hash is a Single Board Computer like Raspberry PI 4 with 2 GB of memory.
Any or all parameters may be specified. The size of the database table 90 may be specified in bits for the index, the seed used to shuffle the lookup table, the number of rounds to shuffle the table, and the size of the resulting hash. Because the LXRHash is parameterized in this way, as computers get faster and larger memory caches, the LXRHash can be set to use 2 GB or 16 GB or more. The Memory bottleneck to computation is much easier to manage than attempts to find computational algorithms that cannot be executed faster and cheaper with custom hardware, or specialty hardware like GPUs. Very large lookup tables will blow the memory caches on pretty much any processor or computer architecture. The size of the database table 90 can be increased to counter improvements in memory caching. The number of bytes in the resulting hash can be increased for more security (greater hash space), without significantly more processing time. LXRHash may even be fast by using small lookup tables. ASIC implementations for small tables would be very easy and very fast. LXRHash only uses iterators (for indexing) shifts, binary ANDs and XORs, and random byte lookups. The use case for LXRHash is Proof of Work (PoW), not cryptographic hashing.
The database table 90 may have equal numbers of every byte value, and shuffled deterministically. When hashing, the bytes from the source data are used to build offsets and state that are in turn used to map the next byte of source. In developing this hash, the goal was to produce very randomized hashes as outputs, with a strong avalanche response to any change to any source byte. This is the prime requirement of PoW. Because of the limited time to perform hashing in a blockchain, collision avoidance is important but not critical. More critical is ensuring engineering the output of the hash isn't possible. Exemplary embodiments yield some interesting qualities. For example, the database table 90 may be any size, so making a version that is ASIC resistant is possible by using very big lookup tables. Such tables blow the processor caches on CPUs and GPUs, making the speed of the hash dependent on random access of memory, not processor power. Using 1 GB lookup table, a very fast ASIC improving hashing is limited to about ˜10% of the computational time for the hash. 90% of the time hashing isn't spent on computation but is spent waiting for memory access. At smaller lookup table sizes, where processor caches work, LXRHash can be modified to be very fast. LXRHash would be an easy ASIC design as it only uses counters, decrements, XORs, and shifts. The hash may be altered by changing the size of the lookup table, the seed, size of the hash produced. Change any parameter and you change the space from which hashes are produced. The Microprocessor in most computer systems accounts for 10× the power requirements of memory. If we consider PoW on a device over time, then LXRHash is estimated to reduce power requirements by about a factor of 10.
Testing has revealed some optimizations. LXRHash is comparatively slow by design (to make PoW CPU bound), but quite a number of use cases don't need PoW, but really just need to validate data matches the hash. So using LXRHash as a hashing function isn't as desirable as simply using it as a PoW function. The somewhat obvious conclusion is that in fact we can use Sha256 as the hash function for applications, and only use the approach as a PoW measure. So in this case, what we do is change how we compute the PoW of a hash. So instead of simply looking at the high order bits and saying that the greater the value the greater the difficulty (or the lower the value the lower the difficulty) we instead define an expensive function to calculate the PoW.
Exemplary embodiments may break out PoW measures from cryptographic hashes. The advantage here is that what exactly it means to weigh PoW between miners can be determined apart from the hash that secures a blockchain. Also, a good cryptographic hash provides a much better base from which to randomize PoW even if we are going to use a 1 GB byte map to bound performance by DRAM access. And we could also use past mining, reputation, staking, or other factors to add to PoW at this point.
PoW may be represented as a nice standard sized value. Because exemplary embodiments may use a function to compute the PoW, we can also easily standardize the size of the difficulty. Since bytes that are all 0×FF or all 0x00 are pretty much wasted, we can simply count them and combine that count with the following bytes. This encoding is compact and easily compared to other difficulties in a standard size with plenty of resolution. So with PoW represented as a large number, the bigger the more difficult, the following rules may be followed. Where bit 0 is most significant, and bit 63 is least significant:
Sha256 is very well tested as a cryptographic function, with excellent waterfall properties (meaning odds are very close to 50% that any change in the input will flit any particular bit in the resulting hash). Hashing the data being mined by the miners is pretty fast. If an application chooses to use a different hashing function, that's okay as well.
Exemplary embodiments may consult an electronic database 252 of tables. When the miner system 22 receives the table identifier 250, the miner system 22 may use, call, and/or implement the database table 90 represented by the table identifier 250. The miner system 22 may obtain, read, or retrieve the table identifier 250 specified by the client-side blockchain mining software application 196. The miner system 22 may additionally or alternatively inspect, read, or retrieve the table identifier 250 from the message 202. Once the table identifier 250 is determined, the miner system 22 may identify the corresponding database table 90 by querying the database 252 of tables for the table identifier 250.
Because the database 252 of tables may store or reference many different database tables, exemplary embodiments may dynamically switch or change the database table 90 to suit any objective or performance criterion. Exemplary embodiments may thus need only specify the table identifier 250, and the table identifier 250 may be dynamically changed at any time. The blockchain environment 20 may flexibly switch, change, and evaluate different database tables, merely by changing or modifying the table identifier 250. The blockchain network may thus experiment with different database tables, different difficulty algorithms 48, and/or different proof-of-work algorithms 52 with little or no impact or effect on hashing. Should an experimental scheme prove or become undesirable, for whatever reason(s), the blockchain environment 20 (such as the blockchain network server 28) may distribute, assign, or restore a new/different table identifier 250 (perhaps by updating the client-side blockchain mining software application 196 and/or distributing/broadcasting the message 202, as this disclosure above explains). The blockchain environment 20 may thus dynamically change the database table 90, which may concomitantly change the difficulty algorithm 48 and/or the proof-of-work algorithm 52, for quick evaluation and/or problem resolution.
Exemplary embodiments improve computer functioning. The database table 90 adds cryptographic security by further randomizing the hash value(s) 60 generated by the hashing algorithm 54. Moreover, because the database table 90 may be remotely located and accessed, exemplary embodiments may only specify the table identifier 250. The memory byte size consumed by the proof-of-work (“PoW”) target scheme 34 and/or the client-side blockchain mining software application 196 is reduced. That is, the blockchain network server 28 need not send the entire software program, code, or instructions representing the database table 90 used by the blockchain environment 20. The blockchain environment 20, the blockchain network server 28, and/or the proof-of-work (“POW”) target scheme 34 need only specify the much smaller byte-sized table identifier 250. The blockchain environment 20 need not be burdened with conveying the database table 90 to the miner system 22 and to other mining nodes. The blockchain environment 20 and the communication network 26 convey less packet traffic, so packet travel times and network latency are reduced. Moreover, especially if the miner system 22 outsources table operations, the miner system 22 is relieved from processing/executing the bit swap operation 92 and consumes less electrical power. Again, then, a faster and more expensive graphics processor or even ASIC will not speed up the proof-of-work operation. The conventional central processing unit 36 is adequate, reduces costs, and promotes democratic mining.
Exemplary embodiments improve cryptographic security. If the blockchain environment 20, the proof-of-work (“PoW”) target scheme 34 and/or the client-side blockchain mining software application 196 specifies use of the database table 90, only authorized miners may have access to the actual entries referenced by the database table 90. That is, if miner system 22 is required to perform, implement, or even execute the bit shuffle operation 92, the miner system 22 must have access to the correct database table 90. An unauthorized or rogue entity, in other words, likely could not perform the bit shuffle operation 92 without access to the correct database table 90. Moreover, if the bit shuffle operation 92 is remotely performed from the miner system 22 (such as by the table server 254, as above explained), perhaps not even the authorized miner system 22 need have access to the database table 90. So, even if the miner system 22 is authorized to mine or process blockchain transactions 32 in the blockchain environment 20, the authorized miner system 22 may still be blind to the database table 90. The authorized miner system 22, in other words, is operationally reliant on the table server 254 to perform the bit shuffle operation 92 that may be required for the difficulty algorithm 48 and/or for the proof-of-work algorithm 52. The miner system 22 simply cannot solve the mathematical puzzle 62 without the table service 258 provided by the table server 254. The database table 90 may thus be proprietary to the blockchain environment 20, but, unknown and unavailable to even the authorized miner system 22 for added cryptographic security.
The miner system 22 may thus serve many blockchains. The miner system 22, for example, may mine BITCOIN® and other cryptographic coin transactional records. However, the miner system 22 may also nearly simultaneously mine financial records sent from or associated with a financial institution, inventory/sales/shipping records sent from a retailer, and transactional records sent from an online website. The miner system 22 may participate in multiple blockchain environments 20, thus having the capability to earn additional rewards, while also being less expensive to purchase and to operate.
Exemplary embodiments may win the block 40 of data. If the output 56, the difficulty 50, and/or the PoW result 42 satisfy the PoW target scheme 34, then the miner system 22 may submit the output 56, the difficulty 50, and/or the PoW result 42 to the blockchain network server 28. The miner system 22 may itself determine if the miner system 22 is the first to satisfy the PoW target scheme 34, or the miner system 22 may rely on the blockchain network server 28 to determine the first solution. When the miner system 22 is the first solver, the miner system 22 earns the right to add the block 40 of data to the blockchain 64. However, if the PoW target scheme 34 is not satisfied, the miner system 22 implements a change or modification and repeats.
Exemplary embodiments may be applied to any signaling standard. Most readers are familiar with the smartphone 164 and mobile computing. Exemplary embodiments may be applied to any communications device using the Global System for Mobile (GSM) communications signaling standard, the Time Division Multiple Access (TDMA) signaling standard, the Code Division Multiple Access (CDMA) signaling standard, the “dual-mode” GSM-ANSI Interoperability Team (GAIT) signaling standard, or any variant of the GSM/CDMA/TDMA signaling standard. Exemplary embodiments may also be applied to other standards, such as the I.E.E.E. 802 family of standards, the Industrial, Scientific, and Medical band of the electromagnetic spectrum, BLUETOOTH®, low-power or near-field, and any other standard or value.
Exemplary embodiments may be physically embodied on or in a computer-readable storage medium. This computer-readable medium, for example, may include CD-ROM, DVD, tape, cassette, floppy disk, optical disk, memory card, memory drive, and large-capacity disks. This computer-readable medium, or media, could be distributed to end-subscribers, licensees, and assignees. A computer program product comprises processor-executable instructions for processing or mining the blockchain transactions 32, as the above paragraphs explain.
While the exemplary embodiments have been described with respect to various features, aspects, and embodiments, those skilled and unskilled in the art will recognize the exemplary embodiments are not so limited. Other variations, modifications, and alternative embodiments may be made without departing from the spirit and scope of the exemplary embodiments.
This patent application is a continuation of U.S. application Ser. No. 17/037,995 filed Sep. 30, 2020, which claims priority from each of the following provisional applications: Provisional Application No. 63/061,372 filed Aug. 5, 2020, U.S. Provisional Application No. 62/962,486 filed Jan. 17, 2020, and U.S. Provisional Application No. 62/963,217 filed Jan. 20, 2020, with all these patent applications incorporated herein by reference in their entireties.
| Number | Name | Date | Kind |
|---|---|---|---|
| 4309569 | Merkle | Jan 1982 | A |
| 5499294 | Friedman | Mar 1996 | A |
| 5606609 | Houser | Feb 1997 | A |
| 5862218 | Steinberg | Jan 1999 | A |
| 5920629 | Rosen | Jul 1999 | A |
| 5966446 | Davis | Oct 1999 | A |
| 6363481 | Hardjono | Mar 2002 | B1 |
| 7028263 | Maguire | Apr 2006 | B2 |
| 7212808 | Engstrom | May 2007 | B2 |
| 7272179 | Siemens | Sep 2007 | B2 |
| 7572179 | Choi | Aug 2009 | B2 |
| 7729950 | Mendizabal | Jun 2010 | B2 |
| 7730113 | Payette | Jun 2010 | B1 |
| 8245038 | Golle | Aug 2012 | B2 |
| 8266439 | Haber | Sep 2012 | B2 |
| 8359361 | Thornton | Jan 2013 | B2 |
| 8442903 | Zadoorian | May 2013 | B2 |
| 8560722 | Gates | Oct 2013 | B2 |
| 8612477 | Becker | Dec 2013 | B2 |
| 8706616 | Flynn | Apr 2014 | B1 |
| 8712887 | Degroeve | Apr 2014 | B2 |
| 8867741 | Mccorkindale | Oct 2014 | B2 |
| 8943332 | Horne | Jan 2015 | B2 |
| 8990322 | Cai | Mar 2015 | B2 |
| 9124423 | Jennas, II | Sep 2015 | B2 |
| 9378343 | David | Jun 2016 | B1 |
| 9396006 | Kundu | Jul 2016 | B2 |
| 9398018 | Macgregor | Jul 2016 | B2 |
| 9407431 | Bellare | Aug 2016 | B2 |
| 9411524 | O'Hare | Aug 2016 | B2 |
| 9411976 | Irvine | Aug 2016 | B2 |
| 9411982 | Dippenaar | Aug 2016 | B1 |
| 9424576 | Vandervort | Aug 2016 | B2 |
| 9436923 | Sriram | Sep 2016 | B1 |
| 9436935 | Hudon | Sep 2016 | B2 |
| 9472069 | Roskowski | Oct 2016 | B2 |
| 9489827 | Quinn | Nov 2016 | B2 |
| 9584493 | Leavy | Feb 2017 | B1 |
| 9588790 | Wagner | Mar 2017 | B1 |
| 9647977 | Levasseur | May 2017 | B2 |
| 9722790 | Ebrahimi | Aug 2017 | B2 |
| 9818109 | Loh | Nov 2017 | B2 |
| 9830580 | Macgregor | Nov 2017 | B2 |
| 9875510 | Kasper | Jan 2018 | B1 |
| 9876646 | Ebrahimi | Jan 2018 | B2 |
| 9882918 | Ford | Jan 2018 | B1 |
| 10025941 | Griffin | Jul 2018 | B1 |
| 10046228 | Tran | Aug 2018 | B2 |
| 10102265 | Madisetti | Oct 2018 | B1 |
| 10102526 | Madisetti | Oct 2018 | B1 |
| 10108954 | Dunlevy | Oct 2018 | B2 |
| 10135607 | Roets | Nov 2018 | B1 |
| 10163080 | Chow | Dec 2018 | B2 |
| 10270599 | Nadeau | Apr 2019 | B2 |
| 10346815 | Glover | Jul 2019 | B2 |
| 10355869 | Bisti | Jul 2019 | B2 |
| 10366204 | Tanner, Jr. | Jul 2019 | B2 |
| 10373129 | James | Aug 2019 | B1 |
| 10411897 | Paolini-Subramanya | Sep 2019 | B2 |
| 10419225 | Deery | Sep 2019 | B2 |
| 10438285 | Konstantinides | Oct 2019 | B1 |
| 10476847 | Smith | Nov 2019 | B1 |
| 10532268 | Tran | Jan 2020 | B2 |
| 10586270 | Reddy | Mar 2020 | B2 |
| 10628268 | Baruch | Apr 2020 | B1 |
| 10685399 | Snow | Jun 2020 | B2 |
| 10693652 | Nadeau | Jun 2020 | B2 |
| 10749848 | Voell | Aug 2020 | B2 |
| 10764752 | Avetisov | Sep 2020 | B1 |
| 10783164 | Snow | Sep 2020 | B2 |
| 10817873 | Paolini-Subramanya | Oct 2020 | B2 |
| 10826685 | Campagna | Nov 2020 | B1 |
| 10855446 | Ow | Dec 2020 | B2 |
| 10873457 | Beaudoin | Dec 2020 | B1 |
| 10915895 | Fogg | Feb 2021 | B1 |
| 10929842 | Arvanaghi | Feb 2021 | B1 |
| 10949926 | Call | Mar 2021 | B1 |
| 10956973 | Chang | Mar 2021 | B1 |
| 10958418 | Ajoy | Mar 2021 | B2 |
| 10997159 | Iwama | May 2021 | B2 |
| 11042871 | Snow | Jun 2021 | B2 |
| 11044095 | Lynde | Jun 2021 | B2 |
| 11044097 | Snow | Jun 2021 | B2 |
| 11044100 | Deery | Jun 2021 | B2 |
| 11063770 | Peng | Jul 2021 | B1 |
| 11075744 | Tormasov | Jul 2021 | B2 |
| 11093933 | Peng | Aug 2021 | B1 |
| 11134120 | Snow | Sep 2021 | B2 |
| 11164250 | Snow | Nov 2021 | B2 |
| 11164254 | Gordon, III | Nov 2021 | B1 |
| 11170366 | Snow | Nov 2021 | B2 |
| 11205172 | Snow | Dec 2021 | B2 |
| 11276056 | Snow | Mar 2022 | B2 |
| 11295296 | Snow | Apr 2022 | B2 |
| 11296889 | Snow | Apr 2022 | B2 |
| 11328290 | Snow | May 2022 | B2 |
| 11334874 | Snow | May 2022 | B2 |
| 11347769 | Snow | May 2022 | B2 |
| 11348097 | Snow | May 2022 | B2 |
| 11348098 | Snow | May 2022 | B2 |
| 11423398 | Mullins | Aug 2022 | B1 |
| 20010029482 | Tealdi | Oct 2001 | A1 |
| 20030018563 | Kilgour | Jan 2003 | A1 |
| 20040085445 | Park | May 2004 | A1 |
| 20050206741 | Raber | Sep 2005 | A1 |
| 20060075228 | Black | Apr 2006 | A1 |
| 20060184443 | Erez | Aug 2006 | A1 |
| 20070027787 | Tripp | Feb 2007 | A1 |
| 20070094272 | Yeh | Apr 2007 | A1 |
| 20070174630 | Shannon | Jul 2007 | A1 |
| 20070296817 | Ebrahimi | Dec 2007 | A1 |
| 20080010466 | Hopper | Jan 2008 | A1 |
| 20080028439 | Shevade | Jan 2008 | A1 |
| 20080059726 | Rozas | Mar 2008 | A1 |
| 20090025063 | Thomas | Jan 2009 | A1 |
| 20090287597 | Bahar | Nov 2009 | A1 |
| 20100049966 | Kato | Feb 2010 | A1 |
| 20100058476 | Isoda | Mar 2010 | A1 |
| 20100161459 | Kass | Jun 2010 | A1 |
| 20100228798 | Kodama | Sep 2010 | A1 |
| 20100241537 | Kass | Sep 2010 | A1 |
| 20110061092 | Bailloeul | Mar 2011 | A1 |
| 20110161674 | Ming | Jun 2011 | A1 |
| 20120203670 | Piersol | Aug 2012 | A1 |
| 20120264520 | Marsland | Oct 2012 | A1 |
| 20130142323 | Chiarella | Jun 2013 | A1 |
| 20130222587 | Roskowski | Aug 2013 | A1 |
| 20130275765 | Lay | Oct 2013 | A1 |
| 20130276058 | Buldas | Oct 2013 | A1 |
| 20140022973 | Kopikare | Jan 2014 | A1 |
| 20140201541 | Paul | Jul 2014 | A1 |
| 20140229738 | Sato | Aug 2014 | A1 |
| 20140282852 | Vestevich | Sep 2014 | A1 |
| 20140289802 | Lee | Sep 2014 | A1 |
| 20140297447 | O'Brien | Oct 2014 | A1 |
| 20140344015 | Puértolas-Montañés | Nov 2014 | A1 |
| 20150193633 | Chida | Jul 2015 | A1 |
| 20150206106 | Yago | Jul 2015 | A1 |
| 20150242835 | Vaughan | Aug 2015 | A1 |
| 20150244729 | Mao | Aug 2015 | A1 |
| 20150309831 | Powers | Oct 2015 | A1 |
| 20150332256 | Minor | Nov 2015 | A1 |
| 20150363769 | Ronca | Dec 2015 | A1 |
| 20150378627 | Kitazawa | Dec 2015 | A1 |
| 20150379484 | Mccarthy | Dec 2015 | A1 |
| 20160002923 | Alobily | Jan 2016 | A1 |
| 20160012240 | Smith | Jan 2016 | A1 |
| 20160021743 | Pai | Jan 2016 | A1 |
| 20160071096 | Rosca | Mar 2016 | A1 |
| 20160098578 | Hincker | Apr 2016 | A1 |
| 20160119134 | Kotaro | Apr 2016 | A1 |
| 20160148198 | Kelley | May 2016 | A1 |
| 20160162897 | Feeney | Jun 2016 | A1 |
| 20160217436 | Brama | Jul 2016 | A1 |
| 20160239653 | Loughlin-Mchugh | Aug 2016 | A1 |
| 20160253663 | Clark | Sep 2016 | A1 |
| 20160260091 | Tobias | Sep 2016 | A1 |
| 20160267472 | Lingham | Sep 2016 | A1 |
| 20160267558 | Bonnell | Sep 2016 | A1 |
| 20160275294 | Irvine | Sep 2016 | A1 |
| 20160283920 | Fisher | Sep 2016 | A1 |
| 20160292396 | Akerwall | Oct 2016 | A1 |
| 20160292672 | Fay | Oct 2016 | A1 |
| 20160292680 | Wilson, Jr. | Oct 2016 | A1 |
| 20160294783 | Piqueras Jover | Oct 2016 | A1 |
| 20160300200 | Brown | Oct 2016 | A1 |
| 20160300234 | Moss-Pultz | Oct 2016 | A1 |
| 20160321675 | Mccoy | Nov 2016 | A1 |
| 20160321751 | Creighton, IV | Nov 2016 | A1 |
| 20160321769 | Mccoy | Nov 2016 | A1 |
| 20160328791 | Parsells | Nov 2016 | A1 |
| 20160330031 | Drego | Nov 2016 | A1 |
| 20160330244 | Denton | Nov 2016 | A1 |
| 20160337119 | Hosaka | Nov 2016 | A1 |
| 20160342977 | Jeremy | Nov 2016 | A1 |
| 20160342989 | Davis | Nov 2016 | A1 |
| 20160344737 | Anton | Nov 2016 | A1 |
| 20160371771 | Serrano | Dec 2016 | A1 |
| 20170000613 | Lerf | Jan 2017 | A1 |
| 20170005797 | Lanc | Jan 2017 | A1 |
| 20170005804 | Zinder | Jan 2017 | A1 |
| 20170033933 | Haber | Feb 2017 | A1 |
| 20170053249 | Tunnell | Feb 2017 | A1 |
| 20170061396 | Melika | Mar 2017 | A1 |
| 20170075938 | Black | Mar 2017 | A1 |
| 20170103167 | Shah | Apr 2017 | A1 |
| 20170124534 | Savolainen | May 2017 | A1 |
| 20170124535 | Juels | May 2017 | A1 |
| 20170134162 | Code | May 2017 | A1 |
| 20170148016 | Davis | May 2017 | A1 |
| 20170161439 | Raduchel | Jun 2017 | A1 |
| 20170177898 | Dillenberger | Jun 2017 | A1 |
| 20170178237 | Wong | Jun 2017 | A1 |
| 20170213287 | Bruno | Jul 2017 | A1 |
| 20170221052 | Sheng | Aug 2017 | A1 |
| 20170228731 | Sheng | Aug 2017 | A1 |
| 20170236123 | Ali | Aug 2017 | A1 |
| 20170243208 | Kurian | Aug 2017 | A1 |
| 20170243289 | Rufo | Aug 2017 | A1 |
| 20170244757 | Castinado | Aug 2017 | A1 |
| 20170330279 | Ponzone | Nov 2017 | A1 |
| 20170344983 | Muftic | Nov 2017 | A1 |
| 20170346693 | Dix | Nov 2017 | A1 |
| 20170352031 | Collin | Dec 2017 | A1 |
| 20170353309 | Gray | Dec 2017 | A1 |
| 20170359374 | Smith | Dec 2017 | A1 |
| 20170364642 | Bogdanowicz | Dec 2017 | A1 |
| 20170373859 | Shors | Dec 2017 | A1 |
| 20180005186 | Hunn | Jan 2018 | A1 |
| 20180048599 | Arghandiwal | Feb 2018 | A1 |
| 20180075239 | Boutnaru | Mar 2018 | A1 |
| 20180075527 | Nagla | Mar 2018 | A1 |
| 20180082043 | Witchey | Mar 2018 | A1 |
| 20180088928 | Smith | Mar 2018 | A1 |
| 20180091524 | Setty | Mar 2018 | A1 |
| 20180097779 | Karame | Apr 2018 | A1 |
| 20180101701 | Barinov | Apr 2018 | A1 |
| 20180101842 | Ventura | Apr 2018 | A1 |
| 20180108024 | Greco | Apr 2018 | A1 |
| 20180117446 | Tran | May 2018 | A1 |
| 20180123779 | Zhang | May 2018 | A1 |
| 20180139042 | Binning | May 2018 | A1 |
| 20180144292 | Mattingly | May 2018 | A1 |
| 20180157700 | Roberts | Jun 2018 | A1 |
| 20180158034 | Hunt | Jun 2018 | A1 |
| 20180167201 | Naqvi | Jun 2018 | A1 |
| 20180173906 | Rodriguez | Jun 2018 | A1 |
| 20180176017 | Rodriguez | Jun 2018 | A1 |
| 20180181768 | Leporini | Jun 2018 | A1 |
| 20180182042 | Vishwa | Jun 2018 | A1 |
| 20180189333 | Childress | Jul 2018 | A1 |
| 20180189781 | Mccann | Jul 2018 | A1 |
| 20180204213 | Zappier | Jul 2018 | A1 |
| 20180219683 | Deery | Aug 2018 | A1 |
| 20180219685 | Deery | Aug 2018 | A1 |
| 20180225640 | Chapman | Aug 2018 | A1 |
| 20180225649 | Babar | Aug 2018 | A1 |
| 20180241565 | Paolini-Subramanya | Aug 2018 | A1 |
| 20180260888 | Paolini-Subramanya | Sep 2018 | A1 |
| 20180260889 | Paolini-Subramanya | Sep 2018 | A1 |
| 20180268162 | Dillenberger | Sep 2018 | A1 |
| 20180268382 | Wasserman | Sep 2018 | A1 |
| 20180268504 | Paolini-Subramanya | Sep 2018 | A1 |
| 20180276270 | Bisbee | Sep 2018 | A1 |
| 20180276668 | Li | Sep 2018 | A1 |
| 20180276745 | Paolini-Subramanya | Sep 2018 | A1 |
| 20180285879 | Gadnis | Oct 2018 | A1 |
| 20180285970 | Snow | Oct 2018 | A1 |
| 20180285971 | Rosenoer | Oct 2018 | A1 |
| 20180288022 | Madisetti | Oct 2018 | A1 |
| 20180315051 | Hurley | Nov 2018 | A1 |
| 20180316502 | Nadeau | Nov 2018 | A1 |
| 20180356236 | Lawrenson | Dec 2018 | A1 |
| 20180365201 | Hunn | Dec 2018 | A1 |
| 20180365686 | Kondo | Dec 2018 | A1 |
| 20180365764 | Nelson | Dec 2018 | A1 |
| 20180367298 | Wright | Dec 2018 | A1 |
| 20190012637 | Gillen | Jan 2019 | A1 |
| 20190013948 | Mercuri | Jan 2019 | A1 |
| 20190018947 | Li | Jan 2019 | A1 |
| 20190028273 | Harras | Jan 2019 | A1 |
| 20190034459 | Qiu | Jan 2019 | A1 |
| 20190036887 | Miller | Jan 2019 | A1 |
| 20190036957 | Smith | Jan 2019 | A1 |
| 20190043048 | Wright | Feb 2019 | A1 |
| 20190044727 | Scott | Feb 2019 | A1 |
| 20190050855 | Martino | Feb 2019 | A1 |
| 20190057382 | Wright | Feb 2019 | A1 |
| 20190065709 | Salomon | Feb 2019 | A1 |
| 20190073666 | Ortiz | Mar 2019 | A1 |
| 20190080284 | Kim | Mar 2019 | A1 |
| 20190081793 | Martino | Mar 2019 | A1 |
| 20190081796 | Chow | Mar 2019 | A1 |
| 20190087446 | Sharma | Mar 2019 | A1 |
| 20190123889 | Schmidt-Karaca | Apr 2019 | A1 |
| 20190132350 | Smith | May 2019 | A1 |
| 20190188699 | Thibodeau | Jun 2019 | A1 |
| 20190197532 | Jayachandran | Jun 2019 | A1 |
| 20190205563 | Gonzales, Jr. | Jul 2019 | A1 |
| 20190236286 | Scriber | Aug 2019 | A1 |
| 20190251557 | Jin | Aug 2019 | A1 |
| 20190253240 | Treat | Aug 2019 | A1 |
| 20190253258 | Thekadath | Aug 2019 | A1 |
| 20190268141 | Pandurangan | Aug 2019 | A1 |
| 20190268163 | Nadeau | Aug 2019 | A1 |
| 20190281259 | Palazzolo | Sep 2019 | A1 |
| 20190287107 | Gaur | Sep 2019 | A1 |
| 20190287199 | Messerges | Sep 2019 | A1 |
| 20190287200 | Schuler | Sep 2019 | A1 |
| 20190288832 | Dang | Sep 2019 | A1 |
| 20190296915 | Lancashire | Sep 2019 | A1 |
| 20190303623 | Reddy | Oct 2019 | A1 |
| 20190303887 | Wright | Oct 2019 | A1 |
| 20190306150 | Immanuel | Oct 2019 | A1 |
| 20190311357 | Madisetti | Oct 2019 | A1 |
| 20190324867 | Tang | Oct 2019 | A1 |
| 20190332691 | Beadles | Oct 2019 | A1 |
| 20190333054 | Cona | Oct 2019 | A1 |
| 20190334715 | Gray | Oct 2019 | A1 |
| 20190334912 | Sloane | Oct 2019 | A1 |
| 20190340586 | Sheng | Nov 2019 | A1 |
| 20190340607 | Lynn | Nov 2019 | A1 |
| 20190342422 | Li | Nov 2019 | A1 |
| 20190347444 | Lowagie | Nov 2019 | A1 |
| 20190347628 | Al-Naji | Nov 2019 | A1 |
| 20190349190 | Smith | Nov 2019 | A1 |
| 20190349426 | Smith | Nov 2019 | A1 |
| 20190354606 | Snow | Nov 2019 | A1 |
| 20190354607 | Snow | Nov 2019 | A1 |
| 20190354611 | Snow | Nov 2019 | A1 |
| 20190354724 | Lowagie | Nov 2019 | A1 |
| 20190354725 | Lowagie | Nov 2019 | A1 |
| 20190354964 | Snow | Nov 2019 | A1 |
| 20190356733 | Snow | Nov 2019 | A1 |
| 20190361917 | Tran | Nov 2019 | A1 |
| 20190372770 | Xu | Dec 2019 | A1 |
| 20190378128 | Moore | Dec 2019 | A1 |
| 20190385165 | Castinado | Dec 2019 | A1 |
| 20190386940 | Hong | Dec 2019 | A1 |
| 20190391540 | Westervelt | Dec 2019 | A1 |
| 20190391858 | Studnicka | Dec 2019 | A1 |
| 20190394044 | Snow | Dec 2019 | A1 |
| 20190394048 | Deery | Dec 2019 | A1 |
| 20200004263 | Dalla Libera | Jan 2020 | A1 |
| 20200004946 | Gilpin | Jan 2020 | A1 |
| 20200005290 | Madisetti | Jan 2020 | A1 |
| 20200019937 | Edwards | Jan 2020 | A1 |
| 20200034571 | Fett | Jan 2020 | A1 |
| 20200034813 | Calinog | Jan 2020 | A1 |
| 20200042635 | Douglass | Feb 2020 | A1 |
| 20200042960 | Cook | Feb 2020 | A1 |
| 20200042982 | Snow | Feb 2020 | A1 |
| 20200042983 | Snow | Feb 2020 | A1 |
| 20200042984 | Snow | Feb 2020 | A1 |
| 20200042985 | Snow | Feb 2020 | A1 |
| 20200042986 | Snow | Feb 2020 | A1 |
| 20200042987 | Snow | Feb 2020 | A1 |
| 20200042988 | Snow | Feb 2020 | A1 |
| 20200042990 | Snow | Feb 2020 | A1 |
| 20200042995 | Snow | Feb 2020 | A1 |
| 20200044827 | Snow | Feb 2020 | A1 |
| 20200044856 | Lynde | Feb 2020 | A1 |
| 20200044857 | Snow | Feb 2020 | A1 |
| 20200065761 | Tatchell | Feb 2020 | A1 |
| 20200067907 | Avetisov | Feb 2020 | A1 |
| 20200075056 | Yang | Mar 2020 | A1 |
| 20200089690 | Qiu | Mar 2020 | A1 |
| 20200099524 | Schiatti | Mar 2020 | A1 |
| 20200099534 | Lowagie | Mar 2020 | A1 |
| 20200104712 | Katz | Apr 2020 | A1 |
| 20200118068 | Turetsky | Apr 2020 | A1 |
| 20200127812 | Schuler | Apr 2020 | A1 |
| 20200134760 | Messerges | Apr 2020 | A1 |
| 20200145219 | Sebastian | May 2020 | A1 |
| 20200167870 | Isaacson | May 2020 | A1 |
| 20200175506 | Snow | Jun 2020 | A1 |
| 20200195441 | Suen | Jun 2020 | A1 |
| 20200211011 | Anderson | Jul 2020 | A1 |
| 20200234386 | Blackman | Jul 2020 | A1 |
| 20200258061 | Beadles | Aug 2020 | A1 |
| 20200279324 | Snow | Sep 2020 | A1 |
| 20200279325 | Snow | Sep 2020 | A1 |
| 20200279326 | Snow | Sep 2020 | A1 |
| 20200280447 | Snow | Sep 2020 | A1 |
| 20200302433 | Green | Sep 2020 | A1 |
| 20200314648 | Cao | Oct 2020 | A1 |
| 20200320097 | Snow | Oct 2020 | A1 |
| 20200320514 | Snow | Oct 2020 | A1 |
| 20200320521 | Snow | Oct 2020 | A1 |
| 20200320522 | Snow | Oct 2020 | A1 |
| 20200320620 | Snow | Oct 2020 | A1 |
| 20200382480 | Isaacson | Dec 2020 | A1 |
| 20200389294 | Soundararajan | Dec 2020 | A1 |
| 20210035092 | Pierce | Feb 2021 | A1 |
| 20210042758 | Durvasula | Feb 2021 | A1 |
| 20210044976 | Avetisov | Feb 2021 | A1 |
| 20210073212 | Conley | Mar 2021 | A1 |
| 20210073750 | Stephen | Mar 2021 | A1 |
| 20210090076 | Wright | Mar 2021 | A1 |
| 20210097602 | Eichel | Apr 2021 | A1 |
| 20210119785 | Ben-Reuven | Apr 2021 | A1 |
| 20210144149 | Simons | May 2021 | A1 |
| 20210174353 | Snow | Jun 2021 | A1 |
| 20210200653 | Jetzfellner | Jul 2021 | A1 |
| 20210201321 | Studnitzer | Jul 2021 | A1 |
| 20210201328 | Gunther | Jul 2021 | A1 |
| 20210226769 | Snow | Jul 2021 | A1 |
| 20210226773 | Snow | Jul 2021 | A1 |
| 20210241282 | Gu | Aug 2021 | A1 |
| 20210248514 | Cella | Aug 2021 | A1 |
| 20210266167 | Lohe | Aug 2021 | A1 |
| 20210266174 | Snow | Aug 2021 | A1 |
| 20210272103 | Snow | Sep 2021 | A1 |
| 20210273810 | Lynde | Sep 2021 | A1 |
| 20210273816 | Deery | Sep 2021 | A1 |
| 20210326815 | Brody | Oct 2021 | A1 |
| 20210328804 | Snow | Oct 2021 | A1 |
| 20210342836 | Cella | Nov 2021 | A1 |
| 20210366586 | Ryan | Nov 2021 | A1 |
| 20220006641 | Snow | Jan 2022 | A1 |
| 20220012731 | Derosa-Grund | Jan 2022 | A1 |
| 20220019559 | Snow | Jan 2022 | A1 |
| 20220020001 | Snow | Jan 2022 | A1 |
| 20220023742 | Tran | Jan 2022 | A1 |
| 20220027893 | Snow | Jan 2022 | A1 |
| 20220027897 | Snow | Jan 2022 | A1 |
| 20220027994 | Snow | Jan 2022 | A1 |
| 20220027995 | Snow | Jan 2022 | A1 |
| 20220027996 | Snow | Jan 2022 | A1 |
| 20220029805 | Snow | Jan 2022 | A1 |
| 20220030054 | Snow | Jan 2022 | A1 |
| 20220034004 | Snow | Feb 2022 | A1 |
| 20220040557 | Tran | Feb 2022 | A1 |
| 20220043831 | Douglass | Feb 2022 | A1 |
| 20220058622 | Snow | Feb 2022 | A1 |
| 20220058623 | Snow | Feb 2022 | A1 |
| 20220083991 | Kemper | Mar 2022 | A1 |
| 20220103341 | Snow | Mar 2022 | A1 |
| 20220103343 | Snow | Mar 2022 | A1 |
| 20220103344 | Snow | Mar 2022 | A1 |
| 20220103364 | Snow | Mar 2022 | A1 |
| 20220141231 | Simons | May 2022 | A1 |
| 20220156737 | Wright | May 2022 | A1 |
| 20220172207 | Cella | Jun 2022 | A1 |
| 20220173893 | Basu | Jun 2022 | A1 |
| 20220198554 | Filter | Jun 2022 | A1 |
| 20220215389 | Balaraman | Jul 2022 | A1 |
| 20220245626 | Sewell | Aug 2022 | A1 |
| 20230147204 | Snow | May 2023 | A1 |
| Number | Date | Country |
|---|---|---|
| 107392618 | Nov 2017 | CN |
| 110392052 | Oct 2019 | CN |
| 110599147 | Dec 2019 | CN |
| 112329041 | Feb 2021 | CN |
| 10128728 | Jan 2003 | DE |
| 3726438 | Oct 2020 | EP |
| 3862947 | Aug 2021 | EP |
| S5383297 | Jul 1978 | JP |
| 2021152931 | Sep 2021 | JP |
| 100653512 | Dec 2006 | KR |
| 1747221 | May 2017 | KR |
| 101747221 | Jun 2017 | KR |
| 0049797 | Aug 2000 | WO |
| 2007069176 | Jun 2007 | WO |
| 2015077378 | May 2015 | WO |
| 2017190795 | Nov 2017 | WO |
| 2018013898 | Jan 2018 | WO |
| 2018109010 | Jun 2018 | WO |
| 2018127923 | Jul 2018 | WO |
| 2018127923072018 | Jul 2018 | WO |
| 2019180702 | Sep 2019 | WO |
| 2019207504 | Oct 2019 | WO |
| 2020125839 | Jun 2020 | WO |
| Entry |
|---|
| Watanabe, Hiroki, et al. “Blockchain contract: Securing a blockchain applied to smart contracts.” 2016 IEEE International Conference on Consumer Electronics (ICCE). IEEE, 2016. |
| Crosby, Michael et al., “BlockChain Technology, Beyond Bitcoin”, Sutardja Center for Entrepreneurship & Technology, Berkeley Engineering, Oct. 16, 2015, 35 pages. |
| Alsolami, Fahad, and Terrance E. Boult. “CloudStash: using secret-sharing scheme to secure data, not keys, in multi-clouds.” Information Technology: New Generations (ITNG), 2015 11th International Conference on. IEEE, 2014. |
| Chakravorty, ANtorweep, and Chunming Rong, “Ushare: user controlled social media based on blockchain.” Proceedings of the 11th International Conference on Ubiquitous Information Management and Communication. ACM, 2017. |
| Chen, Zhixong, and Yixuan Zhu. “Personal Archive Service System using Blockchain Technology: Case Study, Promising and Challenging.” AI & Mobile Services (AIMS), 2017 IEEE International Conference on. IEEE, 2017. |
| Al-Naji, Nader et al., “Basis: A Price-Stable Cryptocurrency with an Algorithmic Central Bank” www.basis.io Jun. 20, 2017, 27 pages. |
| Unknown, “Federated Learning: Collaborative Machine Learning without Centralized Training Data” Apr. 6, 2017, 11 pages. |
| Casey, “BitBeat: Factom Touts Blockchain Tool for Keeping Record Keepers Honest”, Wall Street Journal, Nov. 5, 2014. |
| Menezes, Alfred. J., et al. “Handbook of Applied Cryptography,” 1997, CRC Press, p. 527-28. |
| White, Ron, “How Computers Work,” Oct. 2003, QUE, Seventh Edition (Year: 2003), 23 pages. |
| Luu et al., Making Smart Contracts Smarter, 2016. |
| Feng and Luo, “Evaluating Memory-Hard Proof-of-Work Algorithms on Three Processors,” PVLDB, 13(6): 898-911, 2020. |
| Luther, “Do We Need A “Fedcoin” Cryptocurrency?,” ValueWalk, Newstex Global Business Blogs, Dec. 30, 2015 (Year: 2015). |
| Iddo Bentov, Bitcoin and Secure Computation with Money, May 2016 (Year: 2016). |
| United States: New Generation cryptocurrency, USDX Protocol, Offers Crypto Advantages and Fiat Pegging, Apr. 2, 2018 (Year: 2018). |
| Ana Reyna et al.; On blockchain and its integration with IoT. Challenges and opportunities. Future generation computer systems. vol. 88, Nov. 2018, pp. 173-190. https://www.sciencedirect.com/science/article/pii/S0167739X17329205 (Year: 2018). |
| Krol, Michal et al., “SPOC: Secure Payments for Outsourced Computations” https://arxiv.org/pdf/1807.06462.pdf. (Year: 2018). |
| Written Opinion in PCT/US2021/040207, Inventor Snow, dated Oct. 7, 2021, 14 pages. |
| Unknown, Xtrade White Paper, https://xtrade1-9649.kxcdn.com/wp-content/uploads/2017/09/xtrade-whitepaper.pdf Feb. 7, 2018, 37 pages. |
| Dai et al. TrialChain: A Blockchain-Based Platform to Validate Data Integrity in Large, Biomedical Research Studies arXiv: 1807.03662 Jul. 10, 2018 (Year: 2018). |
| Why offchain storage is needed for blockchain_V4_1 FINAL (Year: 2018), by IBM, 13 pages. |
| Eberhardt et al., “ZoKrates—Scalable Privacy-Preserving Off-Chain Computations,” https://ieeeexplore.ieee.org/stamp/JSP?tp:::&armumber:::8726497. (Year:2018). |
| Muhamed et al. EduCTX: A Blockchain-Based Higher Education Credit Platform, https://ieeexplore.ieee.org/stamp/stamp.jsp?amnumber=8247166. (Year: 2017). 16 pages. |
| Ernandez-Carames et al.; A Review on the Use of Blockchain for the Internet of Things. https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8370027 (Year 2018). 23 pages. |
| ZoKrates—Scalable Privacy-Preserving Off-Chain Computations, by Jacob Eberhardt, Stefan Tai , 8 pages, Nov. 3, 2011 (Year: 2011). |
| Sokolowski, R. (2011). Signed, sealed, delivered: EMortgages are protected from unauthorized alteration by something called a tamper seal. Mortgage Banking, 71(6), 108(4). Retrieved from https://dialog.proquest.com/professional/docview/1068158815? accountid=131444 (Year: 2011). |
| Kroeger, T. et al., The Case for Distributed Data Archival Using Secret Splitting with Percival, 6th International Symposium on Resilient Control Systems (available at IEEE Xplore), p. 204-209 (Year: 2013). |
| “Money in programmable applications: Cross-sector perspectives from the German economy”, Deutsche Bundesbank Eurosystem, https://www.bundesbank.de, 18 pages, 2020. |
| Merkle Mountain Ranges (MMRs)—Grin Documentation, https://quentinlesceller.github.io/grin-docs/technical/building-blocks/merkle-mountain-ranges/, 5 pages, printed Jun. 1, 2022. |
| Merkle Mountain Ranges, https://github.com/opentimestamps/opentimestamps-server/blob/master/doc/merkle-mountain-range.md, 3 pages, printed Jun. 1, 2022. |
| Michelson, Kyle, et al., “Accumulate: An identity-based blockchain protocol with cross-chain support, human-readable addresses, and key management capabilities”, Accumulate Whitepaper, v1.0, Jun. 12, 2022, 28 pages. |
| MOF-BC: A Memory Optimized and Flexible BlockChain for Large Scale Networks. lle:///C:/Users/eoussir/Documents/e-Red%20Folder/16905961/NPL_MOF_BC_A%20Memory%20Optimized%20and%20Flexible%20Blockchain.pdf (Year:2018) 43 pages. |
| On blockchain and its integration with IoT. Challenges and opportunities. file:///C:/Users/eoussir/Downloads/1-s2.0S0167739X17329205-main%20(1). pdf (Year: 2018) 18 pages. |
| Office Action (Non-Final Rejection) dated Jul. 6, 2023 for U.S. Appl. No. 17/365,951 (pp. 1-32). |
| Office Action dated May 9, 2023 for U.S. Appl. No. 17/942,270 (pp. 1-13). |
| Number | Date | Country | |
|---|---|---|---|
| 20220286273 A1 | Sep 2022 | US |
| Number | Date | Country | |
|---|---|---|---|
| 63061372 | Aug 2020 | US | |
| 62963217 | Jan 2020 | US | |
| 62962486 | Jan 2020 | US |
| Number | Date | Country | |
|---|---|---|---|
| Parent | 17037995 | Sep 2020 | US |
| Child | 17751864 | US |
