The present techniques relate to processing queries. More specifically, the techniques relate to processing queries in databases encrypted using fully homomorphic encryption (FHE). Homomorphic encryption is a form of encryption that permits users to perform computations on its encrypted data without first decrypting the encrypted data. These resulting computations are left in an encrypted form which, when decrypted, result in an identical output to that produced had the operations been performed on the unencrypted data. Fully homomorphic encryption (FHE) is a homomorphic encryption scheme that allows the evaluation of arbitrary circuits composed of multiple types of gates of unbounded depth, and is the strongest notion of homomorphic encryption. The fourth generation of FHE, known as the CKKS scheme, released in 2017 by Cheon, Kim, Kim, and Song, supports approximate arithmetic over the encrypted values which are treated as real numbers with some precision. Like other schemes, CKKS also includes bootstrapping of the ciphertext after enough products have been computed, in order to enable more products as required by the computation.
In encrypted database solutions, there is a trade-off between security and usability. This trade-off leads to a situation where some solutions may reduce security to achieve usability. However, such an approach may be dangerous and can lead to information leakage. Current practical solutions for encrypted DB mostly guarantee security against snapshot attackers. For example, these solutions may assume that a snapshot attacker can access to the server only at certain point in time. However, such solutions may not be practical attack model for a client-server database, in which the server inherently has possession of the data over time and can keep logs.
On the other hand, there are some solutions that do not leak any information, such as those solutions that may use fully homeomorphic encryption (FHE). However, such solutions are often considered too slow for practical use. In particular, such solutions may have a query time that is too long. Additionally, some existing database FHE solutions often use a scheme that encrypts plaintexts in small fields with modulo small prime. These solutions may lead to a difficulty in the execution of aggregation queries such as sum, count, average and standard deviation. In particular, for these solutions, aggregation may very expensive, even a simple SUM operation may be inefficient if mod 2 fields are used. Moreover, multiplication operations may be particularly inefficient. In other solutions, using mod p for a relative small p and Fermat's little theorem, the plain text domain may be limited.
According to an embodiment described herein, a system can include processor to receive a preprocessed query from a client device for a fully homomorphic encryption (FHE) encrypted database. The processor can also further execute the preprocessed query on the FHE encrypted database to generate a response. The processor can also transmit the response to the client device. Thus, the system provides a more efficient processing of queries on FHE encrypted databases using client pre-processing. Optionally, the preprocessed query includes negated bit values of the binary representation of a value being compared by a query corresponding to the preprocessed query. In this embodiment, generating the negated bit values at a client device may increase efficiency. Preferably, the preprocessed query is executed using an optimized hybrid bitwise comparison function that includes a squaring of a summation of bitwise comparisons. In this embodiment, processing the negated bit values at a client device may substantially increase efficiency. Optionally, the query includes a comparison. In this embodiment, a comparison query may be efficiently processed.
According to an embodiment described herein, a system can include processor to receive a query from a client device for a fully homomorphic encryption (FHE) encrypted database. The processor can also further partially process the query using the FHE encrypted database. The processor can also transmit the partially-processed query to the client device. Thus, the method provides a more efficient processing of queries on FHE encrypted databases by using client post-processing. Optionally, the processor is to calculate a sum of ciphertexts to be included in the partially-processed query. In this embodiment, the server device may save resources by having the client device decrypt the ciphertexts. Optionally, the processor is to calculate a count to be included in the partially-processed query. In this embodiment, the count may be used to more efficiently calculate queries at the client device. Optionally, the query includes an average query, and the processor is to calculate a sum and a count for the standard deviation query. In this embodiment, the sum and count may be used to more efficiently calculate queries at the client device. In this embodiment, an average query may be efficiently processed. Optionally, the query includes a standard deviation query, where the processor is to calculate a sum and a count for the standard deviation query. In this embodiment, a standard deviation query may be efficiently processed.
According to another embodiment described herein, a method can include receiving, via a processor, an input query for a fully homomorphic encryption (FHE) encrypted database. The method can further include preprocessing, via the processor, the query to generate a preprocessed query. The method can also further include transmitting, via the processor, the preprocessed query to a server with the fully homomorphic encrypted database. The method can also include receiving, via the processor, a response to the preprocessed query from the server. Thus, the method provides a more efficient processing of queries on FHE encrypted databases using client pre-processing. Optionally, preprocessing the query includes calculating negated bit values of the binary representation of a value being compared by a query corresponding to the preprocessed query. In this embodiment, processing the negated bit values at the client device may increase efficiency. Preferably, the negated bit values are to be used in an optimized hybrid bitwise comparison function. In this embodiment, processing the negated bit values in an optimized hybrid bitwise comparison function substantially increases efficiency. Optionally, the input query includes a comparison. In this embodiment, comparison functions may be executed in a more efficient manner.
According to another embodiment described herein, a method can include receiving, via a processor, a query for a fully homomorphic encrypted database. The method can further include transmitting, via the processor, the query to a server with the fully homomorphic encrypted database. The method can also further include receiving, via the processor, a partially-processed query from the server. The method can also include post-processing, via the processor, the partially-processed query to generate a response to the query. Thus, the method provides a more efficient processing of queries on FHE encrypted databases using client post-processing. Preferably, post-processing the partially-processed query includes decrypting the partially-processed query. In this embodiment, operations not able to be fully performed on the server may be achieved by decryption at the client device. Preferably, post-processing the partially-processed query includes summing decrypted values of the partially-processed query. In this embodiment, further efficiency may be achieved by the summation performed at the client device. Optionally, post-processing the partially-processed query includes dividing decrypted values of the partially-processed query by a number received in the partially-processed query. In this embodiment, further efficiency may be achieved by the division performed at the client device. Optionally, post-processing the partially-processed query includes summing values of an indicator vector. In this embodiment, further efficiency may be achieved by the summation at a client device. Optionally, post-processing the partially-processed query includes calculating a division and square root portion of a standard deviation calculation. In this embodiment, further efficiency may be achieved by the division and square root being performed at the client device.
According to another embodiment described herein, a computer program product for querying fully homomorphic encryption (FHE) databases can include computer-readable storage medium having program code embodied therewith. The computer readable storage medium is not a transitory signal per se. The program code executable by a processor to cause the processor to receive a query for a fully homomorphic encrypted database. The program code can also cause the processor to preprocess the query to generate a preprocessed query. The program code can also cause the processor to transmit the preprocessed query to a server with the fully homomorphic encryption (FHE) encrypted database. The program code can also cause the processor to receive a response to the preprocessed query from the server. Thus, the method provides a more efficient processing of queries on FHE encrypted databases using client pre-processing. Optionally, the program code can also cause the processor to post-process the response from the server, where the response includes a partially-processed query. In this embodiment, further efficiency can be achieved by partially processing the query at the server. Optionally, the program code can also cause the processor to also further decrypt the partially-processed query. In this embodiment, operations not able to be fully performed on the server may be achieved by decryption at the client device. Optionally, the program code can also cause the processor to sum decrypted values of the partially-processed query. In this embodiment, further efficiency may be achieved by the summation at the client device. Optionally, the program code can cause the processor to divide decrypted values of the partially-processed query by a number received in the partially-processed query. In this embodiment, further efficiency may be achieved by the division performed at the client device.
With reference now to
In the example of
Still referring to
where ∥Bit 0∥ Bit l| indicates that the same holds for all the bits from 1 to l−1 as well, and every k rows is a block. Thus, rows 1 to k are a 1st block, rows k+1 to 2k are a 2nd block, etc. Because CKKS operates on real numbers and not on finite fields, then, in order to compare two strings of binary ciphertexts ct and a user value v, both of length l, the following hybrid bitwise comparison function might be used:
isEq(a,b)=Πi∈[w](1−(a[i]−b[i])2) Eq. 1
where a=ct, b=v. Eq. 1 generates a value of 1 for each bitwise comparison if a[i]=b[i], or 0 if a[i]≠b[i]. The summation of these bitwise comparisons is used to detect whether the two ciphertexts are similar or not. However, Eq. 1 may not be efficient because a square is calculated for each bitwise comparison. Therefore, in various examples, the client device 102 may send a negated value
isEqOpt(a,b)=(Πi∈[w](a[i]−
Thus, instead of using additional squaring for each two bits, the squaring may be replaced with a single squaring after the multiplication of all the bits. This does not change the result as the intermediate bit comparisons are 0, 1 or −1, the result of the multiplication would be 1 or −1 if the strings are equal and 0 otherwise. The square power ensures that the result would be 1 or 0. Using Eq. 2 instead of Eq. 1 for an equality test spares half of the multiplication and therefore improves running time significantly.
In various examples, a similar optimization can be applied to other comparison functions that use an equality test, such as a greater-than comparison function. For example, a similar optimization can be used as in Eq. 2 because the formula for “isGreater” includes the computation of “isEqual”. Thus, when computing the “isEqual” for “isGreater”, the optimization in Eq. 2 can be used. For example, greater-than can be calculated using the following equation:
In various examples, after comparing the column to a value chosen by the user, an indicators vector may be generated for every block of k rows. For example, the indicators vector may include 1s in matching rows and 0s in non-matching rows. The indicators vector may then be used to filter values in the column that are to be aggregated. In some examples, the filtration can be achieved by multiplying the indicators vector by the aggregated column for every block. The aggregation itself may then be performed. For example, for a regular sum query, the filtrated column may be summed by summing all the cipher texts. After the summation, one cipher text that encrypts k values may be generated. In some examples, the cipher text may be sent to the client device 102 for decryption and summing, as described in
In various examples, to compute a count query, the method described above for calculating a regular sum query may be used. However, in computing the count query, the summation may only be performed on the indicators vector without multiplying the indicators vector by another column.
It is to be understood that the block diagram of
With reference now to
As described above in
Still referring to
In various examples, the system 100B may be used to calculate standard deviation. For example, the following equation may be used to calculate standard deviation:
where X is the filtered column, and N is the number of non-zero elements. In some examples, the server device 104 can compute the SUMs ΣX, ΣX2, and COUNT N with FHE and send the results as partially-processed query 120 to the client device 102. The query post-processor 116 of the client device 102 can then compute the standard deviation with a simple computation, as before. Again, because computing ΣX, ΣX2, and N with FHE may be much more inefficient than decrypting the values and computing these expressions on plaintext, substantial computing resources may be saved at the server device 104 with a small price paid at the user side.
It is to be understood that the block diagram of
In the example of
Still referring to
In various examples, the client device 102 may then decrypt and sum together the values inside the received partially-processed query 207 containing the single block of summed ciphertexts. For example, the decrypter 210 of the client device 102 may decrypt each of the values of ciphertexts to be summed using the key 208. The SUM module 212 may perform a SUM operation on all the decrypted values. The load on the client device 102 may be reduced by only having to perform a single SUM on one block rather than six blocks 206A-206F. In particular, the sum carried out by the post-processing client device 102 has a complexity that is not dependent on the number of records in the database. Therefore, if the number of records in the database is R, then the post-processing optimization at the client device may incur a performance of complexity of order 1. Moreover, by letting the client 102 do the final summation, the server device 104 may also save the processing and time which otherwise may be spent using a “rotate-and-sum” algorithm at the server device 104.
In various examples, several other types of operations may be performed by the system 200. In some examples, the system 200 can execute a COUNT operation using client-side post-processing. For example, the client device 102 can receive the partially-processed query from the server device 104 and sum the indicator vector generated by the server device 104. For example, the indicator vector may be included in the partially-processed query and the summation of the indicator vector may be executed by the client device 102. As one example, the query 202 may thus instead be to COUNT pupils where grade=5.
In some examples, the system 200 can process an averaging (AVG) type query using client-side post-processing. As one example, the query 202 may be a request for an average grade value for pupils whose name begins with the letter “R”. In this examples, the server device 104 may compute an indicator vector for blocks containing students whose name begins with the letter “R”. The values of all the blocks containing such students may similarly be summed together by the server device 104 such that one block of summed values is received by the client device 102. However, the client device 102 may perform the division of the summed values after decrypting the values. Thus, the client device 102 may reduce the burden of performing division associated with working with homomorphic encryption scheme at the server device 104.
In some examples, the system 200 can process a standard deviation (STD) query using client-side post-processing. For example, the standard deviation may be calculated using Equation 4 described above. Again, the server device 104 can compute the SUMs ΣX, ΣX2, and COUNT N with FHE and send the results as partially-processed query to the client device 102. The query post-processor 116 of the client device 102 can then compute the division and square root portions of the standard deviation with less demanding computation. Again, because computing ΣX, ΣX2, and N with FHE may be much more inefficient than decrypting the values and computing these expressions on plaintext, computing resources may be saved overall, and particularly at the server device 104. Moreover, allowing the client device 102 to post-process the SUMs, ΣX, ΣX{circumflex over ( )}2 and COUNT not only reduces runtime, but may also enable the computation of a query which would not be possible otherwise. This is because FHE schemes do not support square root and division operations used in computing some queries, such as the STD query.
It is to be understood that the block diagram of
At block 302, a processor receives an input query for a fully homomorphic encryption (FHE) encrypted database. For example, the input query may be received via a processor of a client device. In various examples, the input query may be a comparison type query.
At block 304, the processor preprocesses the query to generate a preprocessed query. For example, the processor can calculate negated bit values of the binary representation of a value being compared by a query corresponding to the preprocessed query. In various examples, the negated bit values are to be used in an optimized hybrid bitwise comparison function.
At block 306, the preprocessed query is transmitted to a server with the FHE encrypted database. For example, the preprocessed query may be transmitted using any suitable network interface.
At block 308, a response to the preprocessed query is received from the server. For example, the response to a comparison type query may be a ciphertext of encrypted values.
The process flow diagram of
At block 402, a processor receive an input query for a fully homomorphic encrypted database. For example, the processor may receive the input query at a client device. In various examples, the input query may be a COUNT query, a SUM query, an average query, a standard deviation query, among other types of queries.
At block 404, the processor transmits the query to a server with a fully encrypted homomorphic database. For example, the query may be transmitted via any suitable network interface.
At block 406, the processor receives a partially-processed query from the server. For example, the partially-processed query may include an indicator vector. In some examples, the partially-processed query may include summed blocks of ciphertexts.
At block 408, the processor post-processes the partially-processed query to generate a response to the query. For example, the processor can decrypt the partially-processed query. In various examples, the processor can sum decrypted values of the partially-processed query. In some examples, the processor can divide decrypted values of the partially-processed query by a number received in the partially-processed query. In various examples, the processor can sum values of an indicator vector. In some examples, the processor can calculate a division and square root portion of a standard deviation calculation for a standard deviation type query.
The process flow diagram of
At block 502, a processor receives a preprocessed query from a client device for a fully homomorphic encrypted database. For example, the preprocessed query may include negated bit values of the binary representation of the value being compared by the query. As one example, for the query “select SUM(salary) where age=40”, the value 40 is 1010000 in binary representation. Instead of sending (7) encryptions of 1, 0, 1, 0, 0, 0, 0, the negated bit values may be used to send encryptions of 0, 1, 0, 1, 1, 1, 1. In some examples, the preprocessed query may be a comparison type query.
At block 504, the processor executes the preprocessed query on the fully homomorphic encrypted database to generate a response. For example, the preprocessed query may be executed using an optimized hybrid bitwise comparison function that includes a squaring of a summation of bitwise comparisons. In various examples, the negated bit values are to be used in the optimized hybrid bitwise comparison function.
At block 506, the processor transmits the response to the client device. For example, the response to a comparison type query may be a single ciphertext with partially summed values. To continue the example above, the response may be a single ciphertext with partially summed up salaries for the matching persons of age 40.
The process flow diagram of
At block 602, a processor receives a query from a client device for a fully homomorphic encrypted database. For example, the query may be a COUNT query, a SUM query an average type query, a standard deviation type query, or any other similar query.
At block 604, the processor partially processes the query using the fully homomorphic encrypted database. For example, the processor can calculate a sum of ciphertexts to be included in the partially-processed query. In some examples, the processor can calculate a count to be included in the partially-processed query. For example, the count can be calculated by summing an indicator vector generated by the processor.
At block 606, the processor transmits the partially-processed query to the client device. For example, the partially-processed query may include a sum of ciphertexts, a count, or an indicator vector.
The process flow diagram of
It is to be understood that although this disclosure includes a detailed description on cloud computing, implementation of the teachings recited herein are not limited to a cloud computing environment. Rather, embodiments of the present invention are capable of being implemented in conjunction with any other type of computing environment now known or later developed.
Cloud computing is a model of service delivery for enabling convenient, on-demand network access to a shared pool of configurable computing resources (e.g., networks, network bandwidth, servers, processing, memory, storage, applications, virtual machines, and services) that can be rapidly provisioned and released with minimal management effort or interaction with a provider of the service. This cloud model may include at least five characteristics, at least three service models, and at least four deployment models.
Characteristics are as follows:
On-demand self-service: a cloud consumer can unilaterally provision computing capabilities, such as server time and network storage, as needed automatically without requiring human interaction with the service's provider.
Broad network access: capabilities are available over a network and accessed through standard mechanisms that promote use by heterogeneous thin or thick client platforms (e.g., mobile phones, laptops, and PDAs).
Resource pooling: the provider's computing resources are pooled to serve multiple consumers using a multi-tenant model, with different physical and virtual resources dynamically assigned and reassigned according to demand. There is a sense of location independence in that the consumer generally has no control or knowledge over the exact location of the provided resources but may be able to specify location at a higher level of abstraction (e.g., country, state, or datacenter).
Rapid elasticity: capabilities can be rapidly and elastically provisioned, in some cases automatically, to quickly scale out and rapidly released to quickly scale in. To the consumer, the capabilities available for provisioning often appear to be unlimited and can be purchased in any quantity at any time.
Measured service: cloud systems automatically control and optimize resource use by leveraging a metering capability at some level of abstraction appropriate to the type of service (e.g., storage, processing, bandwidth, and active user accounts). Resource usage can be monitored, controlled, and reported, providing transparency for both the provider and consumer of the utilized service.
Service Models are as follows:
Software as a Service (SaaS): the capability provided to the consumer is to use the provider's applications running on a cloud infrastructure. The applications are accessible from various client devices through a thin client interface such as a web browser (e.g., web-based e-mail). The consumer does not manage or control the underlying cloud infrastructure including network, servers, operating systems, storage, or even individual application capabilities, with the possible exception of limited user-specific application configuration settings.
Platform as a Service (PaaS): the capability provided to the consumer is to deploy onto the cloud infrastructure consumer-created or acquired applications created using programming languages and tools supported by the provider. The consumer does not manage or control the underlying cloud infrastructure including networks, servers, operating systems, or storage, but has control over the deployed applications and possibly application hosting environment configurations.
Infrastructure as a Service (IaaS): the capability provided to the consumer is to provision processing, storage, networks, and other fundamental computing resources where the consumer is able to deploy and run arbitrary software, which can include operating systems and applications. The consumer does not manage or control the underlying cloud infrastructure but has control over operating systems, storage, deployed applications, and possibly limited control of select networking components (e.g., host firewalls).
Deployment Models are as follows:
Private cloud: the cloud infrastructure is operated solely for an organization. It may be managed by the organization or a third party and may exist on-premises or off-premises.
Community cloud: the cloud infrastructure is shared by several organizations and supports a specific community that has shared concerns (e.g., mission, security requirements, policy, and compliance considerations). It may be managed by the organizations or a third party and may exist on-premises or off-premises.
Public cloud: the cloud infrastructure is made available to the general public or a large industry group and is owned by an organization selling cloud services.
Hybrid cloud: the cloud infrastructure is a composition of two or more clouds (private, community, or public) that remain unique entities but are bound together by standardized or proprietary technology that enables data and application portability (e.g., cloud bursting for load-balancing between clouds).
A cloud computing environment is service oriented with a focus on statelessness, low coupling, modularity, and semantic interoperability. At the heart of cloud computing is an infrastructure that includes a network of interconnected nodes.
The computing device 700A may include a processor 702 that is to execute stored instructions, a memory device 704 to provide temporary memory space for operations of said instructions during operation. The processor can be a single-core processor, multi-core processor, computing cluster, or any number of other configurations. The memory 704 can include random access memory (RAM), read only memory, flash memory, or any other suitable memory systems.
The processor 702 may be connected through a system interconnect 706 (e.g., PCI®, PCI-Express®, etc.) to an input/output (I/O) device interface 708 adapted to connect the computing device 700 to one or more I/O devices 710. The I/O devices 710 may include, for example, a keyboard and a pointing device, wherein the pointing device may include a touchpad or a touchscreen, among others. The I/O devices 710 may be built-in components of the computing device 700, or may be devices that are externally connected to the computing device 700.
The processor 702 may also be linked through the system interconnect 706 to a display interface 712 adapted to connect the computing device 700 to a display device 714. The display device 714 may include a display screen that is a built-in component of the computing device 700. The display device 714 may also include a computer monitor, television, or projector, among others, that is externally connected to the computing device 700. In addition, a network interface controller (NIC) 716 may be adapted to connect the computing device 700A through the system interconnect 706 to the network 718. In some embodiments, the NIC 716 can transmit data using any suitable interface or protocol, such as the internet small computer system interface, among others. The network 718 may be a cellular network, a radio network, a wide area network (WAN), a local area network (LAN), or the Internet, among others. An external computing device 720 may connect to the computing device 700A through the network 718. In some examples, external computing device 720 may be an external webserver 720. In some examples, external computing device 720 may be a cloud computing node.
The processor 702 may also be linked through the system interconnect 706 to a storage device 722 that can include a hard drive, an optical drive, a USB flash drive, an array of drives, or any combinations thereof. In some examples, the storage device 722 may include a query preprocessor module 724, a query transceiver module 726, and a query post-processor module 728. The query preprocessor module 724 can receive a query for a fully homomorphic encryption (FHE) encrypted database. The query preprocessor module 724 can preprocess the query to generate a preprocessed query. The transceiver module 726 can transmit the preprocessed query to a server with the fully homomorphic encrypted database. The transceiver module 726 can then receive a response to the preprocessed query from the server. In some examples, the transceiver module 726 can transmit the query to a server with the fully homomorphic encrypted database. The transceiver module 726 can then receive a partially-processed query from the server. The query post-processor module 728 can post-process the partially-processed query to generate a response to the query. For example, the query post-processor module 728 can execute a decryption, a multiplication, a division, or a square root on the partially-processed query.
It is to be understood that the block diagram of
It is to be understood that the block diagram of
Referring now to
Referring now to
Hardware and software layer 900 includes hardware and software components. Examples of hardware components include: mainframes 901; RISC (Reduced Instruction Set Computer) architecture based servers 902; servers 903; blade servers 904; storage devices 905; and networks and networking components 906. In some embodiments, software components include network application server software 907 and database software 908.
Virtualization layer 910 provides an abstraction layer from which the following examples of virtual entities may be provided: virtual servers 911; virtual storage 912; virtual networks 913, including virtual private networks; virtual applications and operating systems 914; and virtual clients 915.
In one example, management layer 920 may provide the functions described below. Resource provisioning 921 provides dynamic procurement of computing resources and other resources that are utilized to perform tasks within the cloud computing environment. Metering and Pricing 922 provide cost tracking as resources are utilized within the cloud computing environment, and billing or invoicing for consumption of these resources. In one example, these resources may include application software licenses. Security provides identity verification for cloud consumers and tasks, as well as protection for data and other resources. User portal 923 provides access to the cloud computing environment for consumers and system administrators. Service level management 924 provides cloud computing resource allocation and management such that required service levels are met. Service Level Agreement (SLA) planning and fulfillment 925 provide pre-arrangement for, and procurement of, cloud computing resources for which a future requirement is anticipated in accordance with an SLA.
Workloads layer 930 provides examples of functionality for which the cloud computing environment may be utilized. Examples of workloads and functions which may be provided from this layer include: mapping and navigation 931; software development and lifecycle management 932; virtual classroom education delivery 933; data analytics processing 934; transaction processing 935; and fully homomorphic encryption (FHE) encrypted database processing 936.
The present invention may be a system, a method and/or a computer program product at any possible technical detail level of integration. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention.
The computer readable storage medium can be a tangible device that can retain and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but is not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device such as punch-cards or raised structures in a groove having instructions recorded thereon, and any suitable combination of the foregoing. A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a wire.
Computer readable program instructions described herein can be downloaded to respective computing/processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network, a wide area network and/or a wireless network. The network may comprise copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers and/or edge servers. A network adapter card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium within the respective computing/processing device.
Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages. The computer readable program instructions may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet Service Provider). In some embodiments, electronic circuitry including, for example, programmable logic circuitry, field-programmable gate arrays (FPGA), or programmable logic arrays (PLA) may execute the computer readable program instructions by utilizing state information of the computer readable program instructions to personalize the electronic circuitry, in order to perform aspects of the present invention.
Aspects of the present invention are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the techniques. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, a programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable storage medium having instructions stored therein comprises an article of manufacture including instructions which implement aspects of the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other device to produce a computer implemented process, such that the instructions which execute on the computer, other programmable apparatus, or other device implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
Referring now to
The various software components discussed herein may be stored on the tangible, non-transitory, computer-readable medium 1000A, as indicated in
Referring now to
The various software components discussed herein may be stored on the tangible, non-transitory, computer-readable medium 1000B, as indicated in
The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts or carry out combinations of special purpose hardware and computer instructions. It is to be understood that any number of additional software components not shown in
The descriptions of the various embodiments of the present techniques have been presented for purposes of illustration, but are not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the described embodiments. The terminology used herein was chosen to best explain the principles of the embodiments, the practical application or technical improvement over technologies found in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.