Patent Grant
10728227
In the last several decades, personal computers and other consumer computing devices, such has hand-held devices and smart phones, have become ubiquitous among the general public. As the proliferation of personal computers and other computing devices became prevalent, the usefulness of the computers and other computing devices was increased by interconnected communications between different computers/computing, devices via various electronic networking communications systems. With the advent of the publicly accessible Internet and the establishment of the World Wide Web (WWW) for common communications between computers and/or other computing devices on the Internet, it became common for private identification and financial information to be transferred over the publicly accessible Internet. To ensure that the private information is not accessed by parties that are not intended to be privy to the private information, various encryption techniques have been applied to the private data being transferred over the Internet. As data storage has become accessible over networking technologies, including over the publicly accessible Internet, it has also become prudent to store sensitive data in an encrypted format.
Modern encryption employs mathematical techniques that manipulate positive integers or binary bits. Asymmetric encryption, such as RSA (Rivest-Shamir-Adleman), relies on number theoretic one-way functions that are predictably difficult to factor and can be made more difficult with an ever increasing size of the encryption keys. Symmetric encryption, such as DES (Data Encryption Standard) and AES (Advanced Encryption Standard), uses bit manipulations within registers to shuffle the cryptotext to increase “diffusion” as well as register based operations with a shared key to increase “confusion.” Diffusion and confusion are measures for the increase in statistical entropy on the data payload being transmitted. The concepts of diffusion and confusion in encryption are normally attributed as first being identified by Claude Shannon in the 1940s. Diffusion is generally thought of as complicating the mathematical process of generating unencrypted (plain text) data from the encrypted (cryptotext) data, thus, making it difficult to discover the encryption key of the encryption process by spreading the influence of each piece of the unencrypted (plain) data across several pieces of the encrypted (cryptotext) data. Consequently, an encryption system that has a high degree of diffusion will typically change several characters of the encrypted (cryptotext) data for the change of a single character in the unencrypted (plain) data making it difficult for an attacker to identify changes in the unencrypted (plain) data. Confusion is generally thought of as obscuring the relationship between the unencrypted (plain) data and the encrypted (cryptotext) data. Accordingly, an encryption system that has a high degree of confusion would entail a process that drastically changes the unencrypted (plain) data into the encrypted (cryptotext) data in a way that, even when an attacker knows the operation of the encryption method (such as the public standards of RSA, DES, and/or AES), it is still difficult to deduce the encryption key.
An embodiment of the present invention may comprise a method for the encrypted transfer of numeric message data (M) from a source computing device to a destination computing device, the method comprising: distributing by the source computing device the numeric message data (M) into coefficients of a message multivector (
An embodiment of the present invention may further comprise a method for encrypting numeric message data (M) on a source computing device in order to facilitate transfer of encrypted data from the source computing device to a destination computing device, the method comprising: distributing by the source computing device the numeric message data (M) into coefficients of a message multivector (
An embodiment of the present invention may further comprise a method for decrypting a cryptotext multivector (
An embodiment of the present invention may further comprise a Enhanced Data-Centric Encryption (EDCE) system for the encrypted transfer of numeric message data (M), the EDCE system comprising: a source computing device, wherein the source computing device further comprises: a source numeric message distribution subsystem that distributes the numeric message data (M) into coefficients of a message multivector (
An embodiment of the present invention may further comprise a Enhanced Data-Centric Encryption (EDGE) source computing device for encrypting numeric message data (M) in order to facilitate transfer of encrypted data from the EDCE source computing device to a destination computing device, the EDCE source computing device comprising: a source numeric message distribution subsystem that distributes the numeric message data (M) into coefficients of a message multivector (
An embodiment of the present invention may further comprise a Enhanced Data-Centric Encryption (EDCE) destination computing device for decrypting a cryptotext multivector (
In the drawings,
An embodiment may advantageously utilize Geometric Algebra to provide the encryption and decryption of numeric messages that are to be transmitted through, and possibly have stored by, an intermediary computing system (e.g., the broad-lased computing system currently, and commonly, referred to as the Cloud, or cloud computing). An embodiment of the Geometric Algebra encryption/decryption system that performs the encryption/decryption functions of transferring data securely using Geometric Algebra based encryption/decryption from a source/sender system to a destination/receiver system may be referred to as an Enhanced Data-Centric Encryption (EDGE) system.
Geometric Algebra is an area of mathematics that describes the geometric interaction of vectors and other objects in a context intended to mathematically represent physical interactions of objects in the physical world. The use of Geometric Algebra for cryptography represents a new, manmade use of Geometric Algebra for a purpose entirely outside of the natural basis of Geometric Algebra for representing physical interactions of objects in the real, physical, word. As used herein, this area of mathematics encompasses Geometric Algebra, Conformal Geometric Algebra and Clifford Algebra (referred to collectively herein as “Geometric Algebra”). Generally, Geometric Algebra defines the operations, such as geometric product, inverses and identities, which facilitate many features of embodiments of the example EDGE system embodiments disclosed herein. Further, Geometric Algebra allows for the organization and representation of data into the “payload” of a multivector where the data in the payload may represent, for example, plaintext, cryptotext, or identifying signatures. Consequently, embodiments of the EDCE system make beneficial use of Geometric Algebra properties to provide encryption and decryption operations in a relatively computationally simplistic manner while still providing robust security for both data in motion and data at rest (e.g., data stored in the Cloud).
When encrypted data is transferred through an intermediary computing system, such as is done with cloud based computing, the encrypted data values may be stored on the intermediary computing system until such time a user wants or is attached to the network to allow delivery of the encrypted data value from the source computing device via the intermediary computing system. Alternatively, the encrypted data values may be immediately forwarded to a destination computing device by the intermediary computing system as soon as the subject encrypted data values are received by the intermediary computing system. However, as one skilled in the art will recognize, the process of receiving the encrypted data values at the intermediary computing system inherently includes storing the encrypted data values at the intermediary computing system even if only fleetingly in an immediately used and erased Random Access Memory (RAM) location or operational register location of a computational subsystem of the intermediary computing system.
Embodiments of EDGE system embodiments may be comprised of functional blocks, each of which may be tailored as described in more detail below according to objectives for scope, capability and security. The following sections provide a mathematical and numerical description of these functional blocks.
In order to help minimize the potential confusion of the complex subject matter herein, the descriptions below have been split up to separately cover foundational various topics regarding embodiments of an EDGE system. In view of that, Section 1 provides a general description of embodiments of the foundational operations of an EDGE system. Section 2 provides additional descriptions of embodiments of the foundational EDGE system, including the packing of information into multivectors the encryption and decryption of such multivectors and the unpacking to recover the original information. Appendix A provides a Geometric Algebra. Overview. Generally, in this description, as is the typical convention, for particular examples of operations, Alice and Bob are used for the sending/source and receiving/destination entities, respectively. Thus, the arrangement of the disclosure may be summarized as follows:
Section 1: General EDGE Message Encryption/Decryption
Section 2: Additional Descriptions of EDGE Message Encryption/Decryption
Appendix A: Geometric Algebra Overview
Section 1: General EDCE Message Encryption/Decryption
With the arrival of the interact and many forms of mobile devices, the volume of encrypted data is growing exponentially. Portable devices like “thumb drives,” “smart cards” and Solid State Disks (SSDs) contain both plain text and or encrypted “passive” data storage. Passive data storage is found on the tiny devices for the Internet of Things (IoT) as well as the large memories in server farms.
When data leaves storage, when it is in motion, it is even more vulnerable to attack. Current encryption techniques have not evolved alongside network security infrastructure and they are not well suited for the sheer volume of data in motion. As we move towards “cloud computing,” as mobile devices move us towards “perimeter-less” network security, the industry is moving away from trusting just the security of networks, servers or applications and focusing toward data-centric encryption. With data-centric encryption and authentication there are controls that are traveling with the data rather than just happening at the application layer or the final destination in a network.
However, the fluidity of this data in motion stalls with the computationally intensive mathematics that remain at the heart of current encryption infrastructures. Ciphers such as RSA (Rivest-Shamir-Adieman), DES (Data Encryption Standard) and/or AES (Advanced Encryption Standard) are little more than static “machinery” that bogs down communication efficiency. The actual problem is much bigger. How can robust security be provided when:
An embodiment may be described as enhanced data-centric encryption, or EDGE. Compared to incumbent encryption schemes, EDGE is computationally simplistic while providing robust security over the span of the communication channel. EDGE security is scalable from tiny embedded IoT (Internet of Things) devices up to server farms. EDGE functionality enables many cipher schemes that show speed and bandwidth advantages over current methods. One aspect of EDGE that provides speed enhancement in the encryption/decryption of data is that the EDGE encryption/decryption may be implemented using basic arithmetic operations of addition, subtraction, multiplication, and division. Notably, EDGE does not require a complex operation to select a large prime number, to calculate a logarithm function, to calculate a natural logarithm function, and/or to calculate other complex and computationally intensive mathematical functions (i.e., prime numbers, logarithms, natural logarithms, and/or other complex mathematical Operations are not required in the Geometric Algebra calculations disclosed herein).
A central feature of the various embodiments is the use of Geometric Algebra, an area of mathematics that has not been utilized before in encryption. Geometric Algebra as used herein is an area of mathematics that encompasses Geometric Algebra, Conformal Geometric Algebra and Clifford Algebra (collectively herein, “Geometric Algebra”). Geometric Algebra allows for the organization and representation of data into the “payload” of a multivector where the data may be plaintext, cryptotext, or signatures, for example. Geometric Algebra defines the operations, such as geometric product, inverses and identities, which are the enablers of encryption/decryption calculations of various embodiments.
Multivectors are simply the additive combination of a scalar, a vector, a bi-vector and so forth up to an n-dimension vector. However, the unit vectors follow the algebraic structure of quaternions (Hamilton) and non-commutative algebra (Grassman). These two types of algebra allowed Clifford to conceive of the Geometric Product which is used by the various embodiments as one of the “primitive” functions of the embodiments of EDCE systems.
An example of a two-dimension (2D) multivector A that includes a scalar and a vector is:
Ā=a0+a1ē1+a2ē2+a12ē12
where ēi is a unit vector along the i-axis and ē12 represents the orientation of the area created by a12. The operations of Geometric Algebra on multivectors are discussed more fully in “Appendix A” herein, below, but some general observations may be helpful to the description of the various embodiments disclosed below. First, each of the ai values in the multivector Ā above may be “packed” with information and each ai value may range from zero to very large (e.g., >256,000 bits or an entire message). Secondly, the inverse of Ā when multiplied by Ā yields unity, or:
ĀĀ−1=1
Thus, if a second multivector
ĀĀ−1
For the various embodiments, the “payload” may be packed in the values of the scalars and coefficients of the multivector elements, To ensure that EDCE systems may perform properly, it is necessary to have some limitations on the coefficient values chosen for the multivectors. For instance, the Rationalize operation on multivectors yields zero when all multivector coefficients are equal. Such multivectors having all equal coefficients have no inverse and the geometric product of such multivectors having all equal coefficients with another multivector has no inverse. As discussed in more detail below, the decryption methodology for EDCE systems utilize the inverse of the cryptotext multivector being decrypted and of the security keys) multivector to perform the decryption. Therefore, the cryptotext multivector being decrypted should not have all equal value coefficients. One means to ensure that the cryptotext multivector being decrypted does not have all equal value coefficients is to have the packing/coefficient distribution method ensure that not all coefficients are equal to each other (i.e., at least one coefficient should be different than the other coefficients) when creating the shared security multivector(s) and the data message multivectors. For an embodiment of the EDCE that simply transfers the data message, ensuring that that not all coefficients are equal to each other when creating the shared security multivector(s) and the data message multivectors will ensure that the cryptotext multivector to be decrypted will not have all equivalent coefficients.
Additionally, separate multivectors may be encoded for many purposes, such as a shared secret (defined below), authentication information, and timestamps. In addition to the encryption and decryption of a message, the EDCE multivector format and Geometric Algebra foundation of an EDGE embodiment may enable a single transmission to contain far more than just cryptotext, including dummy data to increase encryption security, command instructions for additional operations, and/or configuration data for the additional operations.
A. Hardware Implementation for EDCE Embodiments (
Further, as shown in
Various embodiments may implement the network/bus communications channel 104 using any communications channel 104 capable of transferring electronic data between the first 102 and second 106 computing devices. For instance, the network/bus communication connection 104 may be an Internet connection routed over one or more different communications channels during transmission from the first 102 to the second 106 computing devices. Likewise, the network bus communication connection 104 may be an internal communications bus of a computing device, or even the internal bus of a processing or memory storage Integrated Circuit (EC) chip, such as a memory chip or a Central Processing Unit (CPU) chip. The network/bus communication channel 104 may utilize any medium capable of transmitting electronic data communications, including, but not limited to: wired communications, wireless electro-magnetic communications, fiber-optic cable communications, light/laser communications, sonic/sound communications, etc., and any combination thereof of the various communication channels.
The various embodiments may provide the control and management functions detailed herein via an application operating on the first 102 and/or second 106 computing devices. The first 102 and/or second 106 computing devices may each be a computer or computer system, or any other electronic devices device capable of performing the communications and computations of an embodiment. The first 102 and second 104 computing devices may include, but are not limited to: a general purpose computer, a laptop/portable computer, a tablet device, a smart phone, an industrial control computer, a data storage system controller, a CPU, a Graphical Processing Unit (GPU), an Application Specific Integrated Circuit (ASI), and/or a Field Programmable Gate Array (FPGA). Notably, the first 102 and second 106 computing devices may be the storage controller of a data storage media (e.g., the controller for a hard disk drive) such that data delivered to/from the data storage media is always encrypted so as to limit the ability of an attacker to ever have access to unencrypted data. Embodiments may be provided as a computer program product which may include a computer-readable, or machine-readable, medium having stored thereon instructions which may be used to program/operate a computer (or other electronic devices) or computer system to perform a process or processes in accordance with the various embodiments. The computer-readable medium may include, but is not limited to, hard disk drives, floppy diskettes, optical disks, Compact Disc Read-Only Memories (CD-ROMs), Digital Versatile Disc ROMS (DVD-ROMs), Universal Serial Bus (USB) memory sticks, magneto-optical disks, ROMs, random access memories (RAMS), Erasable Programmable ROMs (EPROMs), Electrically Erasable Programmable ROMs (EEPROMs), magnetic optical cards, flash memory, or other types of media/machine-readable medium suitable for storing electronic instructions. The computer program instructions may reside and operate on a single computer/electronic device or various portions may be spread over multiple computers/devices that comprise a computer system. Moreover, embodiments may also be downloaded as a computer program product, wherein the program may be transferred from a remote computer to a requesting computer by way of data signals embodied in a carrier wave or other propagation medium via a communication link (e.g., a modem or network connection, including both wired/cabled and wireless connections).
B. General EDGE Operational Flow Charts (
At process 208, the source 202 converts any alphanumeric text in the message into numeric message data (M) based on the alphanumeric encoding protocol (e.g., ASCII, other English language/alphabetic coding systems, foreign language encoding for non-alphabetic languages (e.g., katakana for Japanese), or even pure symbol to numeric values such as for emojii's) of the original text. Again, both the source 202 and destination 204 need to know and use the same alphanumeric text conversion into a numeric value process to ensure that results of both the source 202 and the destination 204 are the same. If the message data is already in numeric form, it is not necessary to perform process 208 as the original numeric message data (M) may be used as is. The various embodiments may perform the encryption process with numeric Message data (M) that is, but are not limited to: positive numbers, negative numbers, zero, integer numbers, and/or real numbers. At process 210, the source 202 distributes the numeric message data (M) into message multivector (
Again, for the various embodiments, the “payload” may be packed in the values of the scalars and coefficients of the multivector elements. To ensure that EDGE systems may perform properly, it is necessary to have some limitations on the coefficient values chosen for the multivectors. For instance, the Rationalize operation on multivectors yields zero when all multivector coefficients are equal. Such multivectors having all equal coefficients have no inverse and the geometric product of such multivectors having all equal coefficients with another multivector has no inverse. As discussed in more detail below, the decryption methodology for EDCE systems utilize the inverse of the cryptotext multivector being decrypted and of the security key(s) multivector to perform the decryption. Therefore, the cryptotext multivector being decrypted should not have all equal value coefficients. One means to ensure that the cryptotext multivector being decrypted does not have all equal value coefficients is to have the packing/coefficient distribution method ensure that not all coefficients are equal to each other (i.e., at least one coefficient should be different than the other coefficients) when creating the shared security multivector(s) and the data message multivectors. For an embodiment of the EDGE that simply transfers the data message, ensuring that that not all coefficients are equal to each other when creating the shared security multivector(s) and the data message multivectors will ensure that the cryptotext multivector to be decrypted will not have all equivalent coefficients.
It is not necessary that the distribution (i.e., “packing”) of the message multivector (
It is noteworthy that the number of potential coefficients is directly related to the size/dimension (N) of the multivectors such that the number of coefficients increases by a factor of 2 (i.e., 2N) for each incremental increase in the size/dimension (N) of the multivector. To increase the confusion and/or diffusion of the encryption process disclosed herein, using multivectors of at least two dimensions will provide at least four coefficients to distribute the numeric data of the message (M) and the shared secret (SS). By increasing the number of dimensions (N) of multivectors beyond two-dimension multivectors, the confusion and/or diffusion security characteristics will also be increased due to the additionally available multivector coefficients. Further, with the additionally available coefficients it is also possible to transfer more data in a single multivector message (
At process 214, the source 202 encrypts a cryptotext multivector (
Due to the nature of the geometric product operation of Geometric Algebra, there are many possible variations of the geometric product application that will provide similar degrees of confusion and diffusion. Some, but not all, of the potential geometric product calculations to encrypt the message data (M) include: a geometric product (
At process 220, the destination 204 receives the cryptotext numeric data (C) sent by the source 202. At process 222, the destination distributes the cryptotext numeric data (C) into the cryptotext multivector (
At process 304, the source computing device distributes the second shared secret key numeric value (SS
At process 306, the source computing device encrypts the cryptotext multivector (
At process 314, the destination computing device also distributes the second shared secret key numeric value (SS
At process 316, the destination computing device decrypts the cryptotext multivector (
At process 404, the source computing device distributes the second shared secret key numeric value (SS
At process 406, the source computing device encrypts the cryptotext multivector (
At process 408, in the process of the source computing device for converting the cryptotext multivector (
The remaining decryption process 226 of the destination 204 of
At process 416, the destination computing device also distributes the second shared secret key numeric value (SS
At process 416, the destination computing device decrypts the cryptotext multivector (
Additionally, while the flow charts and flow chart details described above with respect to
Section 2: Additional Descriptions of EDCE Message Encryption/Decryption
The disclosure below provides a simplified example of the operations and data relationships during the performance of an EDCE, embodiment. The amount of data, the type of data, and the particular data values shown and described in the example are not meant to represent any particular real system, but are provided only for the purpose of showing the operations and data relationships of an embodiment. Further, the embodiments described below are not meant to restrict operations to particular data types, encryption shared secret key exchange techniques, text to numeric and back conversion techniques, and/or number to multivector coefficient assignment techniques.
In addition to the utilization of the Geometric Algebra geometric product as a novel encryption primitive, the various embodiments may be comprised of functional blocks, each of which may be tailored as described according to objectives for scope, capability and security. The following sections provide a mathematical and numerical description of one or more example embodiments of these functional blocks. The numerical results in the examples are generally derived from Geometric Algebra executing in the C programming language.
A. Packing and Unpacking Multivectors
Contents
1) Text to Number
For the example EDCE embodiment described herein, each text message needs to be converted to a number in order to become a valid operational unit for all EDGE computations. For the embodiments shown herein, the numbers are typically shown in base 10, but the various embodiments may choose other number bases as desired by the system designer. For instance, a hex (base 16) representation may provide particular advantages when dealing with ASCII numerical representations as standard ASCII has a representation based on the numbers 0-127 (i.e., 27), which is one power of two (i.e., hex is 28) less than the typical 8 bits represented by a hex number of xFF. According to the ASCII character-encoding scheme, symbols such as the letters a, b, c and so on, are represented in order formats (such as binary, decimal, octets, hexadecimal, etc.), which are described in the ASCII printable code chart, a table that presents the relationship between formats. So the letters “a,” if and “c” in ASCII decimal code are 97, 93 and 99, respectively.
As an example, assume that the plaintext text message is “message.” In ASCII decimal code, this is represented as follows:
With this relationship between symbols and decimal numbers, the conversion from text to number in base 10, using the text “message”, is executed as follows:
The variable n represents the final number of the conversion from text to number. We start defining this variable to zero. So, n=0.
Then we create an array with the ASCII decimal codes for each letter of the message:
This array has a size of 7 elements, thus array size=7
Then, for each value of the array of ASCII characters, in a loop, we will
Note the details of each iteration below:
By performing the above calculation, the final value of a is; 30792318992869221
Thus, the plain text “message” as a number in base 10 is equal to 30792318992869221. Once we have a base 10 number it is possible to perform the calculations described herein for message encryption. If desired, entropy may be added at this step by performing transformations on the ASCII codes, such as addition or modulo operations. No such entropy adding transformations are used in the examples that follow.
2) Number to Text
After performing various calculations, a base 10 number is transmitted and received. From the above example of a message multivector, the coefficients are concatenated to form a number string. The “number to text” conversion process for this number string also uses the ASCII printable code chart, but the recovery routine is different from the “text to number” conversion. The procedure is described below:
We start with the variable s, which is an empty string that will become the final text recovered from the input number, (Note: the symbol “ ” “ ” is from the C-language and means empty string)
s=“ ”
The input number is 30792318992869221.
n=30792318992869221
Now, we perform a loop until n is “emptied”, since this number refers to an actual text message. This means the loop will stop when n is equal to zero. In each loop iteration, we will recover, from the last to the first, each ASCII decimal code correspondent to the text that we are retrieving. To do that, we will perform a bitwise AND operation using the value 0xFF (which is 256-1 in hexadecimal format or in base 16). We will convert the code to character symbols and concatenate with the current string, always putting the most recent recovered character in the front of the string. Lastly, we will update the value of n by performing a right shift of 8 bits.
Let's say that the function “get_char” converts the ASCII decimal code to a character symbol.
The procedure is as follows:
while n>0
s=get_char(n AND 0×FF)+s
Note the details of each iteration below:
Thus, the number 30792318992869221 is converted to the text string “message,” which agrees with the original plaintext.
3) Multivector Data Structure
For the example embodiment discussed herein, any number in base 10 may be a coefficient of a multivector element. A multivector may contain arbitrary data, or data that is a result of a series of operations. A base 10 number may also be represented in multivector form by distributing pieces of this number string to the coefficients in the multivector. Multivectors that are 2D have 4 elements/coefficients available to pack with pieces of this number string, a 3D multivector has 8 elements, and 4D has 16, EDCE, has been demonstrated up to at 7D. A 4D multivector with 16 elements is written as:
Ā=a0+a1e1+a2e2+a3e3+a4e4+a12e12+a13e13+a14e14+a23e23+a24e24+a34e34+a123e123+a124e124+a134e134+a234e234+a1234e1234
4) Number to Multivector
Given the base 10 number string 30792318992869221, this string may be a single coefficient of, say, a 2D multivector, as follows:
0+30792318992869221e1+e2+e12
EDCE has been demonstrated where the number string distributed to an element of the multivector exceeds 4,000 digits. However, the base 10 number in our example will typically be “distributed” in an ad hoc manner across all the multivector elements, such as:
30792+31899e1+28692e2+21e12
The above distribution is called “number to multivector.” For an EDCE embodiment, the method of distributing the number string may be according to any of a variety of algorithms as long as the method is known and used by both the sending and receiving entities. To increase cryptographic “confusion,” the distribution algorithm may include shuffling of the assignments to elements, performing functional operations on numbers assigned to elements or changing the algorithm between messages in a conversation. More operations increase encryption entropy.
Again, for the various embodiments, the “payload” may be packed in the values of the scalars and coefficients of the multivector elements. To ensure that EDCE systems may perform properly, it is necessary to have some limitations on the coefficient values chosen for the multivectors. For instance, the Rationalize operation on multivectors yields zero when all multivector coefficients are equal. Such multivectors having all equal coefficients have no inverse and the geometric product of such multivectors having all equal coefficients with another multivector has no inverse. As discussed in more detail below, the decryption methodology for EDCE systems utilize the inverse of the cryptotext multivector being decrypted and of the security key(s) multivector to perform the decryption. Therefore, the cryptotext multivector being decrypted should not have all equal value coefficients. One means to ensure that the cryptotext multivector being decrypted does not have all equal value coefficients is to have the packing/coefficient distribution method ensure that not all coefficients are equal to each other (i.e., at least one coefficient should be different than the other coefficients) when creating the shared security multivector(s) and the data message multivectors. For an embodiment of the EDGE that simply transfers the data message, ensuring that that not all coefficients are equal to each other when creating the shared security multivector(s) and the data message multivectors will ensure that the cryptotext multivector to be decrypted will not have all equivalent coefficients.
Additionally, separate multivectors may be encoded for many purposes, such as a shared secret (defined below), authentication information, and timestamps. In addition to the encryption and decryption of a message, the EDCE multivector format and Geometric Algebra foundation of an EDCE embodiment may enable a single transmission to contain far more than just cryptotext, including dummy data to increase encryption security, command instructions for additional operations, and/or configuration data for the additional operations.
The simple distribution method used in the EDCE embodiment examples below is described as follows: Let the input base 10 number string 30792318992869221. We count the number of digits and determine that the number size is 17 digits. We then determine how to distribute these digits to the elements of a multivector. Considering a multivector of 2D, which has 4 elements, we apply the following equation:
Where ep is “each portion” length.
Now we have the original base 10 number and its size (17), the multivector structure (2D, 8 elements) and the length of each element (5). Now we need to “slice” the base 10 number in order to distribute each part as a coefficient of the new multivector.
Computationally, the procedure is as follows:
This creates the following multivector:
30792+31899e1+28692e2+21e12
A First Alternative “Number to Multivector” Distribution Method:
To increase entropy, the conversion from number to multivector may include an intermediate step of shuffling the digits of the base 10 number representation.
As before, let the base 10 number=30792318992869221, Even though this number has an odd number of digits (17), it can be split into two sequences as follows:
The sequence may be shuffled to n′ as:
Now, n′ is 99286922130792318, which is the new number to be distributed to the elements of the multivector
The following bitwise operations require a minimum magnitude (>2number of bits in n′) of the numbers involved in order to correctly generate and recover data. To comply with such a requirement, we need to find an exponent b related to the number of bits n′ that has to be a power two. Since the number of bits of n′ is equal to 57, we make b equal to the next power of two number, which turns to be 64. Hence we use 2b=264 as the arithmetic parameter for the binary operations.
Compute n″:
n″=(n′+2b+s1)+2b+s2
n″=(99286922130792318+264+992869221)+264+30792318
n″=33785588866960916932803988894906868159702738740312398462
Converting n″ to multivector would give the following 2D multivector representation:
To recover the original number from the above multivector, the procedure is as follows:
Since the current multivector is 2D, we will recover the sequences in 2 steps. The number of steps is equal to the number of sequences. For recovering the sequences, we will apply the equations bellow, making use of the binary operators AND and >>(right shift).
n″=multivector_to_number (
n″=3378558886696091693203988894906868159702738740312398462
Step 1;
s1=n″ AND (264−1)=30792318
Step 2:
s2=(n″>>64) AND (264−1)=992869221
Now, concatenate the sequences to recover the original n=30792318992869221.
A Second Alternative “Number to Multivector” Distribution Method:
Another relationship for packing the coefficients of the multivector is to ensure that the coefficients of the multivector representation of the plaintext numeric message follow a mathematical data organization between the value of the plaintext numeric message and at least one of the values of the coefficients of the multivector representation of the plaintext numeric message where the mathematical operations incorporating the one or more values of the multivector coefficients have a result equal to the original plaintext numeric message value. The mathematical relationship may include: addition of at least one coefficient of the multivector coefficients, subtraction of at least one coefficient of the multivector coefficients, addition of a constant value, subtraction of a constant value, multiplication of at least one coefficient of the multivector coefficients by a constant value, and division of at least one coefficient of the multivector coefficients by a constant value. The location of the various mathematical operations relative to the particular locations of the coefficients in the multivector representation should also be consistently applied to all source numeric data messages converted to a multivector as well as for result multivectors converted to a result numeric data value in a particular encryption/decryption pathway. For example, for a mathematical relationship that includes both addition and subtraction operations, and for a three dimensional multivector which has eight possible coefficients in the multivector representation (e.g., c1, c2, c3, c12, c13, c23, and c123, numbered so as to correspond with the unit vector associated with each coefficient), if the coefficients for the e2 and e12 unit vectors (i.e., c2 and c12) are subtracted in the calculation of the mathematical relationship for a source numeric data message conversion to a multivector, the destination numeric message should also treat the c2 and c12 coefficients as being subtracted when doing a multivector to number conversion with the same mathematical relationship. In fact, obtaining a numeric value from the coefficients of a numeric data message multivector “packed” using a mathematical relationship is relatively simple and straight forward. To obtain the numeric data message value, simply perform the mathematical relationship equation for the numeric data message multivector using the values of the multivector coefficients plugged into the mathematical relationship equation, Other than the location of additions and subtractions within the mathematical relationship of the coefficients, the actual values of the coefficients may be selected as desired by a user so long as the mathematical relationship equals the original numeric value being encrypted, One skilled in the art will recognize that there are many, perhaps even an infinite, number of ways to select coefficient values that meet the stated criteria/restrictions and that each of those ways will create a satisfactory EDCE embodiment so long as the stated criteria/restrictions are, in fact, met.
Handling Special Cases:
Regardless of the method of distribution, the leading digit in any coefficient must be non-zero. For example, let the number to be converted to multivector be 30792318990869221. Applying the distribution method shown above would result in:
30792+31899e1+08692e2+21e12
Note the third element=08692e2. The computer will treat this number as 8692. When converting back from multivector to number, instead of 30,792,318,990,869,221 we would have 3,079,231,899,869,221, which is not the same number (commas added only for comparability).
To avoid this outcome, it is necessary to include verification in the algorithm that the first number of a coefficient is non-zero. If it is zero, this number should be placed as the last number in the coefficient of the previous element of the multivector. So, the correct result of the conversion of the number 30792318990869221 to a 2D multivector is:
30792+318990e1+8692e2+21e12
5) Multivector to Number
The distribution method used in the EDCE embodiment examples below is described as follows:
For the distribution (i.e., “packing”) method disclosed above for parsing the string representation of a base 10 number to obtain the coefficient values, converting a multivector to a base 10 number is simply the reverse process of concatenating the coefficients of the multivector in order to form a base 10 number.
As an example:
Note that in the core EDCE protocol of some of the example embodiments herein, only base 10 number strings are transmitted, not multivectors, but sending only base 10 number strings is not a requirement for an embodiment. In some embodiments, the number may be sent using a numeric variable representation such as an integer or floating point data type. Further, while not typical of most encryption systems, instead of sending a single cryptotext number (C), an embodiment may also simply skip the step of converting the multivector (
B. Shared Secret
A “Shared Secret” is a fundamental element in cryptography. A Shared Secret enables secure communication between two or more parties. For the various embodiments the Shared Secret is a number string of digits that may be packed into a multivector in the manner shown above. The “Shared Secret Multivector” may be used to operate on other multivectors, such as creating the geometric product of the Shared Secret Multivector and the message multivector.
A variety of methods are already in practice to establish the Shared Secret between sources and destinations. As disclosed herein, the conversion of a “Shared Secret” number to a “Shared Secret Multivector” is completely novel. Communication end-point devices may be “pre-conditioned” with a unique identifier (number string) known only to the system administrators. In a public/private key environment such as RSA, the Shared Secret may be encrypted by the source using only the destination's public key. The method used in the examples below is the Diffie-Hellman key exchange protocol. This is a convenient, widely adopted method to establish a number string Shared Secret. However, any method that securely produces a shared number string is suitable for use with the various embodiments.
The Diffie-Hellman protocol uses the multiplicative group of integers modulo p (see, for example, https://en.wikipedia.org/wiki/Multiplicative_group_of_integers_modulo_n), where p is prime (see, for example, https://en.wikipedia.org/wiki/Prime_number), and g is a primitive root modulo p (see, for example,
https://en.wikipedia.org/wiki/Primitive_root_modulo_n and
https://en.wikipedia.org/wiki/Modular_arithmetic). These two values are chosen in this way to ensure that the resulting shared secret can take on any value from 1 to p−1. A simple example of Diffie-Hellman follows:
Note that Diffie-Hellman protocol is not limited to negotiating a key shared by only two participants. Any number of users can take part in the agreement by performing iterations of the protocol and exchanging intermediate data.
Assume the following:
To compute SA0, Alice's public signature and SB0, Bob's public signature:
SA0=ga mod p
SB0=gb mod p
SA0=49009686585026240237091226039
SB0=28663920458684997936652161962
To compute the shared secret, both Alice and Bob will perform the following equation, which will generate the same value for both, thus the shared secret is reference as SS:
SS=SB0
SS=SA0
SS=374101092446920532886590141005
The shared secret number string above may be distributed as before to create a Shared Secret Multivector:
In a similar manner the SA0 and SB0 number string for Alice and Bob can be distributed in a multivector format to create
C. Cryptotext Creation
The cryptotext is created using the EDGE primitive which is the geometric product of the Message multivector and one or more other multivectors. In the most basic form, the cryptotext multivector may be the geometric product of the Message multivector and the Shared Secret Multivector.
The procedure is defined as follows, Let the plaintext message be “this is a test.” By applying the “text to number” conversion, we will get the plaintext message as the number:
2361031878030638688519054699098996
By applying the “number to multivector” conversion using a 2D multivector structure the plaintext multivector is:
Using the Shared Secret multivector that was determined above:
The cryptotext multivector can be defined as the geometric product:
Using methods for calculating the geometric product of
In order to be transmitted, as a payload,
c10=5649796324893205335999076139905242395250959838376115938268771181474
To increase the entropy of the Cryptotext Multivector, the Geometric Product of the Message Multivector may be taken with more than one other multivector or by using the same multivector twice to form a sandwich or by the addition of left and right multivector operations on the same Shared Secret Multivector. Two examples of these types are
Note that there are several alternative methods to construct the Cryptotext Multivector. One alternative is to encrypt the plaintext message using a conventional symmetric cipher such as AES, converting the number string output of that cipher to multivector format and use this multivector in calculating the geometric product with
D. Decryption
Since Bob has the same shared secret of the source, he can open the cryptotext by performing a geometric product of the cryptotext multivector and the inverse of the shared secret multivector. When Bob receives C10, he will apply the appropriate number to multivector conversion to get:
To recover the plaintext multivector
The method to determine
Thus,
The multivector
M10=2361031878030638688519054699098996
Finally, this number is converted to text using the “number to text” procedure described above, resulting in:
Mplain text=“this is a test”
E. EDCE Flow Chart (
Setup (502): The sequence is initiated by establishing the signature and shared secret multivectors. Here the Diffie-Hellman procedure 508 is shown but other asymmetric key ciphers such as RSA may be used to generate a number string known only to the source 504 and the destination 506. Alternatively, end-point devices may be “pre-conditioned” with a secret (number string) known to the system administrator from which the session multivectors may be constructed. The Diffie-Hillman procedure 508 sets up/creates the shared secret keys 510 and then the setup 502 creates multivectors of the Diffie-Hillman keys 510 in the multivector setup 512.
Source (504): The Message Multivector 516 is constructed at the create message operation 514 by concatenating the message ASCII code string to a number string and then distributing that number to the coefficients of the message multivector at 514. The method of distributing to coefficients uses a prescribed algorithm known and used by both the source 504 and the destination 506.
The Message Multivector 516 is then encrypted 518 by computing the geometric product of the message and Shared Secret multivectors.
Destination (506): C (532) is received through a user-defined operation 530 and converted back to the Cryptotext Multivector 536 using the prescribed distribution method 534. The destination 506 computes the multivector inverse of the Shared Secret Multivector and uses this result in the decrypt equations 538 such as
Setup (602): The sequence is initiated by establishing the signature and shared secret multivectors, here the Diffie-Hellman procedure 608 is shown but other asymmetric key ciphers such as RSA may be used to generate a number string known only to the source 604 and the destination 606. Alternatively, end-point devices may be “pre-conditioned” with a secret (number string) known to the system administrator from which the session multivectors may be constructed. The Diffie-Hillman procedure 608 sets up/creates the shared secret keys 610 and then the setup 602 creates multivectors 612 of the Diffie-Hillman keys in the multivector setup 612.
Source (604): The Message Multivector 616 is constructed at the create message operation 614 by concatenating the message ASCII code string to a number string and then distributing that number to the coefficients of the message multivector at 614, The method of distributing to coefficients uses a prescribed algorithm known and used by both the source 604 and the destination 606.
The Message Multivector 616 is then encrypted 618 by computing the geometric product of the message and Shared Secret multivectors.
Destination (606): C (632) is received through a user-defined operation 630 and converted back to the Cryptotext Multivector 636 using the prescribed distribution method 634. this result in the decrypt equations 638 such as
F. Symmetric Key Pair Encryption/Decryption from 0-Blade Reduction Operation (
In order to increase security to the Geometric Algebra encryption primitives, a pair of symmetric shared secret keys may be used instead of a single shared secret key. The following lists the processes that may be used to generate/extract/obtain the second shared secret multivector (
Geometric Algebra Encryption Primitives
Primitive 1—“Sandwich”/Triple Product
Encryption
The first encryption primitive can be created through a sequence of geometric products using the pair of keys generated via the 0-Blade Reduction Operation (described herein, above) as follows:
Decryption
The decryption process uses the previously defined inverse multivector as follows:
Primitive 2—Multivector based Sylvester's Equation
Encryption
The well-known Sylvester's equation is employed here to generate a second encryption primitive which also uses the pair of symmetric encryption keys generated via the 0-Blade Reduction Operation (described herein, above) as follows:
Decryption
The decryption operation involves the closed-form solution of the Sylvester's equation for 3-dimensional multivector space as follows:
Note that a solution for higher dimensions requires a different formula. Further note that if the original shared secret (SS) is generated using an encrypted/secure key exchange, such as, but not limited to the Diffie-Hellman process discussed in more detail herein, there is no transmission of the original shared secret multivector (
Numerical Examples for Encryption and Decryption With Doubled Shared-Secret in 3 Dimensions
Let the message multivector
and the original secret multivector
From the original secret multivector
scalar=(
scalar=2281454761
Then create the second secret multivector
Geometric Product “Sandwich” or Geometric Triple Product
In order to encrypt the multivector
and recover the message multivector
Multivector Based Sylvester's Equation
Another way to encrypt the message multivector
and recover the message multivector
G. An Unbreakable Primitive Using Geometric Algebra and Arithmetic Functions Example with Secret Sharing and 3D Multivectors
Set Up
A multivector may act as a Geometric Algebra object such that components of multi-dimensions and Clifford k-vectors are present. An example is:
which shows the components:
a0scalar−known as 0-blade or 0-vector
a1ē1+a2ē2+a3ē33D vector or 1-blade or vector
a12ē12+a23ē23+a31ē312-blade or bi-vector
a123ē1233-blade or tri-vector
A typical, but not the only, arithmetic function used for secret sharing is the Diffie-Hellman function, which is based on cyclic groups with element g; for example:
SS=gab mod p
where SS is a shared secret which can be used by both the source and destination sides and where the operation gab mod p yields SS. This is standard in the cyber security field.
Unbreakable Primitive
Given a message M, distribute the numerical content of M over a multivector
M=m1,m2,m3 . . . mn
such that mi is a number that constitutes a placed integer value for a coefficient. Then:
Note that other multivector variations are also possible.
The shared secret SS is changed to a multivector in the same or a similar manner, such as:
SS=s11,s12,s13 . . . s1n
An operation known as “0-Blade Reduction” creates a new scalar from
SS
Then SS
Finally, the multivector-based Sylvester's equation may be used to create a cipher. Thus, the cryptotext multivector C is:
because SS
Up to this point the encryption may have susceptibility to a pair of known cryptotext attacks. However, as shown in part below, the final unbreakability has been achieved.
Encryption Primitives With Unbreakable Cipher:
Primitive 1—“Sandwich”/Triple Product
Encryption
The first encryption primitive may be created through a sequence of geometric products using the pair of keys generated via the 0-Blade Reduction Operation (described above) as follows:
In order to add another layer of security to the cipher text
Decryption
The decryption process may comprise the following steps:
Primitive 2—Multivector-Based Sylvester's Equation
Encryption
The multivector based Sylvester's equation may be employed here to generate a second encryption primitive which also uses the pair of symmetric shared secret keys generated via the 0-Blade Reduction Operation (described above), as follows:
As was done above for the encryption primitive with “sandwich”/triple product, it may be beneficial to add another layer of security by using the same process as described above for XOR masking.
The cipher multivector
Decryption
The decryption operation involves the closed-form solution of the multivector based Sylvester's equation for 3-dimensional multivector space and the XOR ‘unmask’ previously described for the “sandwich”/triple product above. The summarized processes are given below:
Note that a solution for higher dimensions requires a different formula. Further note that if the original shared secret (SS) is generated using an encrypted/secure key exchange, such as, but not limited to the Diffie-Hellman process discussed in more detail herein, there is no transmission of the original shared secret multivector (
Geometric Algebra combines the work of Hamilton (Quartenion) and Grassman (Non-Commutative Algebra) into a field that generalizes the product of two vectors, including the 3-dimensionally restricted “Cross Product” to an n-dimensional subspace of the vector space (V) over number fields (,
,
,
, etc.) such that the subspace is a product space that allows two vectors to have a “geometric product” as:
Where Ā and
For a simple pair of two dimensional vectors:
Ā=a1ē1+a2ē2
where the set {ē1, ē2} are unit basis vectors and {ai}, {bi}, i=1,2 are scalars, the geometric product follows the rules of Geometric Algebra, as described below:
ēiēi=0
ēiēj=−ēj
ēi
ēiēj=ēij (compact notation)
ēi·ēi=1
ēi·ēj=0
Thus, by performing the geometric product of Ā and
Resulting in:
Āē2
The product Āē2 which in compact notation is written as ē12 and represents an area created by ē1
ē2 clockwise rotation or −ē2
ē1 in anti-clockwise. The orientation is given by the sign of the term in front of the ē1
ē2 component.
As an example, let
Ā=−2ē1+4ē2
Using the rules of Geometric Algebra described above we can compute the geometric product between Ā and
Another way of computing the geometric product between multivectors combines the rules of the dot and the wedge products shown above, where we define the following rules when expanding a general geometric product:
ēiēi=1
ēiēj=−ējēi
ēiēj=ēij (compact notation)
This method is used for computer coding in order to speed up the computation of the geometric product. Using the same multivectors of the previous example and these rules, the geometric product between Ā and
Definition of Multivectors and Blades
Another way of describing the objects (or elements) that form a multivector is to use the definition of “blade”, or a k-blade. In this convention at k=0, we have a scalar, at k=1 a vector, k=2 a bivector, and so on,
A multivector is then formed by:C
0+
C
1+
C
2+ . . . +
C
2
where n is the dimension of the multivector.
As was shown in the previous example, the Geometric Product of two 1-blade multivectors yields a 0-blade plus 2-blade multivector as a result:
Note that if one wishes to multiply a scalar t by a multivector
tC
0+t
C
1+t
C
2+ . . . t
C
n
For the particular example above one would have:
t
The dimensionality of a vector or k-blades in general is not restricted or a function of k. For example, we could easily demonstrate the example above with 3-D, 4-D or n-D vectors, such as Ā=a1ē1+a2ē2+a3ē3+ . . . +anēn, which would yield “hypercubes” as elements of the blades created from the wedge product part of the resulting multivectors.
Multivector Operations
Embodiments may rely in part upon the unique characteristics of Geometric Algebra multivector operations. Key among these operations is:
Where Ā−1 is the inverse of Ā. There are several important multivector operations that are applied to determine k-blade multivector inversions:
The norm of a multivector Ā is defined as
∥01/2
where the operator 0 pick only the elements of the 0-blade of the resulting vector of the geometric product between Ā and its reverse. It is also the result of the dot product between Ā and its reverse. As an example, define the 2 dimension multivector
The reverse of Ā is:
The norm of Ā is computed from:
Thus,
∥ĀĀ†
01/2=√{square root over (a02+a12+a22+a122)}
The amplitude of a 2-blade or 3-blade multivector is computed as:
|Ā|=(Ā
As an example consider a 2-blade multivector:
Ā=2+5ē1+3ē2+8ē12
The Clifford conjugation of Ā is defined as:
The amplitude of Ā can be found by first computing the geometric product Ā
Hence,
|Ā|=(Ā
Multivector inversion is defined as:
This gives:
As an example consider again the multivector Ā=2+5ē1+3ē2+8ē12 has its inverse computed as:
Hence,
which being equal to 1, clearly shows that the inverse is thus proven.
For the special case where the multivector reduces to the sub-algebra of 1-blade, the inverse can be also computed using the reverse through the following relationship:
For example, consider the multivector:
Ā=5ē1+3ē2+8ē3
The reverse in this case is:
Ā†=5ē1+3ē2+8ē3
which is identical to the original multivector. If we compute the inverse, we have
Because:
ĀĀ†=25ē1ē1+15ē1ē2+40ē1ē3+15ē2ē1+9ē2ē2+24ē2ē3+40ē3ē1+24ē3ē2+64ē3ē3
ĀĀ†=25+15ē12−40ē31−15ē12+9+24ē23+40ē31−24ē23+64
ĀĀ†=99
Thus if we compute ĀĀ−1 we obtain:
For application purposes we wish to have a single formula to compute the inverse and we choose the first option, which uses the Clifford conjugation operation. However, when computing the inverse of a given multivector that is reduced to the even sub-algebra it is possible to obtain a complex-like number from the geometric product between Ā
by multiplying top and bottom by the complex conjugate x−iy which produces a single real valued denominator
This process can be duplicated for a multivector where now the reverse operation (†) will play the role of the complex conjugate. This allows us to rewrite the inverse equation for a multivector as follows:
As an example of the use of this general formula let:
Ā=2+3ē1+4ē2+6ē3+7ē12+8ē23+9ē31+10ē123
Its Clifford conjugation is given by:
Using the properties of geometric product described earlier we compute Ā
Ā
Using the original inverse formula defined by the Clifford conjugation we would have
This result is clearly a complex-like number, since (ē123)2=i2=(√{square root over (−1)})2=−1. We rationalize the denominator by performing a geometric product on top and bottom with its reverse (Ā
The use of multivector inverses is important to the various embodiments. The algorithms in Geometric. Algebra used to compute inverses vary according to the space dimension n of the multivector. This overview of Geometric Algebra is not intended to be exhaustive, only sufficient for the discussion of the embodiment features and the examples herein presented. For a more exhaustive reference see [REFERENCE1].
The use of Sylvester's Equation in Sign, Seal, Delivered Messaging
In the method of “signing and sealing” cryptotext we make use of a well-known matrix equation in the field of mathematics called the Sylvester's equation [REFERENCE2], which is given by
C=AX+XB
By knowing the matrices A, B, and C it is possible to calculate a unique solution for the matrix X. For our purposes, we use the analogous definition of the Sylvester's equation for multivectors as shown in [REFERENCE1]
which is obtained when defining a linear function over multivectors in the form of:
Here the elements of the Sylvester's equation are defined as:
A solution analogous to the results using quartenions or matrices in [REFERENCE2] is given in [REFERENCE1] as:
and is used by the destination to unpack the cryptotext prior to the decryption process.
The foregoing description of the invention has been presented for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise form disclosed, and other modifications and variations may be possible in light of the above teachings. The embodiment was chosen and described in order to best explain the principles of the invention and its practical application to thereby enable others skilled in the art to best utilize the invention in various embodiments and various modifications as are suited to the particular use contemplated. It is intended that the appended statements of the invention be construed to include other alternative embodiments of the invention except insofar as limited by the prior art.
This application is based upon and claims priority to U.S. provisional application Ser. No. 62/370,183, filed Aug. 2, 2016, entitled “Methods and Systems for Enhanced Data-Centric Encryption Systems Using Geometric Algebra;” Ser. No. 62/452,246, filed Jan. 30, 2017, entitled “Methods and Systems for Enhanced Data-Centric Encryption Additive Homomorphic Systems Using Geometric Algebra;” and Ser. No. 62/483,227, filed Apr. 7, 2017, entitled “Methods and Systems for Enhanced Data-Centric Scalar Multiplicative Homomorphic Encryption Systems Using Geometric Algebra;” all of which are specifically incorporated herein by reference for all that they disclose and teach.
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| Number | Date | Country | |
|---|---|---|---|
| 20180041481 A1 | Feb 2018 | US |
| Number | Date | Country | |
|---|---|---|---|
| 62370183 | Aug 2016 | US | |
| 62452246 | Jan 2017 | US | |
| 62483227 | Apr 2017 | US |
