ABSTRACT
Abstract
Provided are methods and systems for performing secure analytics using term generations and a homomorphic encryption. An example method includes receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, wherein the list of indices is generated using the term generation function and the hash function, applying, by the server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices, extracting, by the server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the encrypted data set to obtain an encrypted result, and sending the encrypted result to the client to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of U.S. patent application Ser. No. 16/158,230, filed Oct. 11, 2018. The U.S. patent application Ser. No. 16/158,230 is a continuation-in-part of U.S. patent application Ser. No. 15/875,914, filed Jan. 19, 2018, which claims the benefit of U.S. Provisional Application No. 62/448,890, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,918, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,893, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,906, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,908, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,913, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,916, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,883, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,885, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,902, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,896, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,899, filed on Jan. 20, 2017; and U.S. Provisional Application No. 62/462,818, filed on Feb. 23, 2017. The subject matter of the aforementioned applications is hereby incorporated by reference for all purposes.
TECHNICAL FIELD
This disclosure relates to the technical field of secure data processing. More specifically, this disclosure relates to systems and methods for performing secure analytics using term generation and a homomorphic encryption.
BACKGROUND
With development of computer technologies, sensitive data, such as financial information and medical records can be kept on remote servers or cloud-based computing resources. Authorized users can access the sensitive data through applications running, for example, on their personal computing devices. Typically, personal computing devices are connected, via data networks, to servers or cloud-based computing resources. Therefore, messages sent over the data network can be subject to unauthorized access during the transmission as well as during processing on the remote servers or cloud-based computing resources.
Encryption techniques, such as homomorphic encryption, can be applied to the sensitive data and messages to prevent unauthorized access during the transmission over a network and processing in the data environment. Encryption and decryption may include specific algebraic operations performed on plaintext and/or ciphertext representing queries, data in data sources, and on responses to the queries. Performing the encryption and decryption may require additional time and computing resources of servers. The additional time may cause delays in processing of queries by the servers. Additionally, processing of queries may require accessing data from various data sources with different levels of security.
SUMMARY
This summary is provided to introduce a selection of concepts in a simplified form that are further described in the Detailed Description below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
Generally, the present disclosure is directed to the technology for secure data processing. Some embodiments of the present disclosure may facilitate performing secure analytics using term generation and homomorphic encryption.
According to one example embodiment of the present disclosure, a method for performing secure analytics using a homomorphic encryption is provided. An example method may include receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices. Generation of the list of indices may include extracting, using the term generation function, a set of terms from a query, mapping, using the hash function, the terms in the set of terms to indices in a list of indices, and encrypting the indices in the list with the public key. The method may include applying, by the server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set. The method may also include extracting, by the server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result. The method may include sending, by the server, the encrypted result to the client, where the client can be configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
Extracting the data may include matching indices from the further homomorphically encrypted list of indices to indices from the homomorphically encrypted list of indices. The term generation function is dynamically specified by the client at the time of sending the query to the at least one server. The term generation function can be configured to generate at least one search index for searching a document. The term generation function can be configured to generate at least one phonetic index to search for a word.
The term generation function can be configured to generate a feature vector for searching an image including biometrical data. The term generation function can be configured to generate a feature vector to search for an image of a face. The term generation function can be configured to generate a term for searching data having geospatial attributes. The term generation function can include a Structured Query Language (SQL) processing engine. The term generation function can include a machine learning model trained to generate the set of terms based on the query.
According to one example embodiment of the present disclosure, a system for performing secure analytics using a homomorphic encryption is provided. The system may include at least one processor and a memory storing processor-executable codes, wherein the at least one processor can be configured to implement the operations of the above-mentioned method for performing secure analytics using a homomorphic encryption.
According to yet another example embodiment of the present disclosure, the operations of the above-mentioned method for query processing with adaptive risk decisioning are stored on a machine-readable medium comprising instructions, which when implemented by one or more processors perform the recited method for performing secure analytics using a homomorphic encryption.
Other example embodiments of the disclosure and aspects will become apparent from the following description taken in conjunction with the following drawings.
BRIEF DESCRIPTION OF DRAWINGS
Exemplary embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements.
FIG. 1 shows a block diagram of an example environment suitable for practicing the methods described herein.
FIG. 2 shows a system for homomorphic encryption, according to various embodiments.
FIG. 3 is a block diagram illustrating specifics of homomorphic encryption scheme, according to some example embodiments.
FIG. 4 shows an example scheme of a database including geospatial data associated with usage of mobile devices, according to an example embodiment.
FIG. 5 is an example SQL query executed over the geospatial data, according to an example embodiment.
FIG. 6 is a flow chart of an example method for performing secure analytics using term generation and a homomorphic encryption, according to some example embodiments.
FIG. 7 illustrates an exemplary computer system that may be used to implement some embodiments of the present disclosure.
DETAILED DESCRIPTION
The technology disclosed herein is concerned with methods and systems for query processing. Embodiments of the present disclosure may facilitate processing queries requiring reading data from multiple data sources with different levels of protections.
According to some embodiments of the present disclosure, a system for performing secure analytics using a homomorphic encryption may include at least one server and at least one client. The server can receive, from the client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices. The list of indices can be generated by the client by extracting, using the term generation function, a set of terms from a query mapping, using the hash function, the terms in the set of terms to indices in a list of indices, and encrypting the indices in the list with the public key. The server can apply the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set. The server may extract, using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result. The server can send the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
Referring now to the drawings, various embodiments are described in which like reference numerals represent like parts and assemblies throughout the several views. It should be noted that the reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples outlined in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims.
FIG. 1 shows a block diagram of an example environment 100 (also referred to as system 100 ) suitable for practicing the methods described herein. It should be noted, however, that the environment 100 is just one example and is a simplified embodiment provided for illustrative purposes, and reasonable deviations of this embodiment are possible as will be evident for those skilled in the art.
As shown in FIG. 1 , the environment 100 may include one or more clients 110 1 - 110 M , one or more servers 120 1 - 120 N , and communications links 130 . One or more clients 110 1 - 110 M and one or more servers 120 1 - 120 N can be disposed in same and/or different locations (e.g., offices, data centers, cities, counties, geographic regions, countries, continents, and so forth). Additionally or alternatively, one or more clients 110 1 - 110 M and one or more servers 120 1 - 120 N can each be in varied computing environments, including shared computing architectures, hybrid architectures, distinct architectures (e.g., cloud computing environments), and combinations thereof. One or more clients 110 1 - 110 M and one or more servers 120 1 - 120 N can include any of an application-specific integrated circuit (ASIC), field programmable gate array (FPGA), application-specific standard product (ASSP), an electronic circuit, a processor (shared, dedicated, or group) that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. Additionally or alternatively, one or more clients 110 1 - 110 M and one or more servers 120 1 - 120 N can include at least some of the characteristics of computing systems described further in relation to FIG. 7 .
A target data source may be in a single server or distributed over multiple servers of one or more servers 120 1 - 120 N as target data source 122 1 - 122 N . Target data source 122 1 - 122 N can be unencrypted (in plaintext form), deterministically encrypted (e.g., using Rivest, Shamir, & Adleman (RSA) encryption technology), semantically encrypted (e.g., using Advanced Encryption Standard (AES) technology), and combinations thereof. When target data source 122 1 - 122 N is a combination of encrypted and unencrypted fields, each field can be consistently encrypted or unencrypted. For example, when data source 122 1 - 122 N includes an unencrypted âemployee nameâ field, the employees names are all unencrypted, as opposed to some name names being encrypted and others unencrypted. By way of further non-limiting example, when data source 122 1 - 122 N includes an encrypted âsocial security numberâ field, the social security numbers are all encrypted, as opposed to some social security numbers being encrypted and others unencrypted. Data stored in and/or retrieved from target data source 122 1 - 122 N can be encrypted and/or decrypted as described in relation to FIG. 2 .
Communications links 130 can include various combinations and permutations of wired and wireless networks (e.g., Ethernet, Wi-Fi, Bluetooth, mobile broadband, the Internet, and so forth), internal/external computer busses, and the like, such as described in reference to FIG. 6 . Although depicted as a single âblock,â communications links 130 can be, for example, multiple distinct/separate combinations and permutations of wired and wireless networks, internal/external computer busses, and the like.
In some embodiments, system 100 encrypts a desired query (or analytic) to be executed over target data source 122 1 - 322 N using a homomorphic encryption scheme, such as described with reference to FIG. 2 . For example, system 100 (e.g., one or more clients 110 1 - 110 M ) encrypts the desired query as a homomorphic query matrix (Q_M). Q_M can be encrypted and the desired query should not be recoverable without a private key. For example, the private key can be associated with the keyed hash function. By way of further non-limiting example, the private key is not necessarily associated with the keyed hash function. Since one or more servers 120 1 - 120 N do not decrypt the desired query or the encrypted result E(R), one or more servers 120 1</
CROSS-REFERENCE TO RELATED APPLICATIONS
This application is a continuation-in-part of U.S. patent application Ser. No. 16/158,230, filed Oct. 11, 2018. The U.S. patent application Ser. No. 16/158,230 is a continuation-in-part of U.S. patent application Ser. No. 15/875,914, filed Jan. 19, 2018, which claims the benefit of U.S. Provisional Application No. 62/448,890, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,918, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,893, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,906, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,908, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,913, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,916, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,883, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,885, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,902, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,896, filed on Jan. 20, 2017; U.S. Provisional Application No. 62/448,899, filed on Jan. 20, 2017; and U.S. Provisional Application No. 62/462,818, filed on Feb. 23, 2017. The subject matter of the aforementioned applications is hereby incorporated by reference for all purposes.
TECHNICAL FIELD
This disclosure relates to the technical field of secure data processing. More specifically, this disclosure relates to systems and methods for performing secure analytics using term generation and a homomorphic encryption.
BACKGROUND
With development of computer technologies, sensitive data, such as financial information and medical records can be kept on remote servers or cloud-based computing resources. Authorized users can access the sensitive data through applications running, for example, on their personal computing devices. Typically, personal computing devices are connected, via data networks, to servers or cloud-based computing resources. Therefore, messages sent over the data network can be subject to unauthorized access during the transmission as well as during processing on the remote servers or cloud-based computing resources.
Encryption techniques, such as homomorphic encryption, can be applied to the sensitive data and messages to prevent unauthorized access during the transmission over a network and processing in the data environment. Encryption and decryption may include specific algebraic operations performed on plaintext and/or ciphertext representing queries, data in data sources, and on responses to the queries. Performing the encryption and decryption may require additional time and computing resources of servers. The additional time may cause delays in processing of queries by the servers. Additionally, processing of queries may require accessing data from various data sources with different levels of security.
SUMMARY
This summary is provided to introduce a selection of concepts in a simplified form that are further described in the Detailed Description below. This summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.
Generally, the present disclosure is directed to the technology for secure data processing. Some embodiments of the present disclosure may facilitate performing secure analytics using term generation and homomorphic encryption.
According to one example embodiment of the present disclosure, a method for performing secure analytics using a homomorphic encryption is provided. An example method may include receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices. Generation of the list of indices may include extracting, using the term generation function, a set of terms from a query, mapping, using the hash function, the terms in the set of terms to indices in a list of indices, and encrypting the indices in the list with the public key. The method may include applying, by the server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set. The method may also include extracting, by the server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result. The method may include sending, by the server, the encrypted result to the client, where the client can be configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
Extracting the data may include matching indices from the further homomorphically encrypted list of indices to indices from the homomorphically encrypted list of indices. The term generation function is dynamically specified by the client at the time of sending the query to the at least one server. The term generation function can be configured to generate at least one search index for searching a document. The term generation function can be configured to generate at least one phonetic index to search for a word.
The term generation function can be configured to generate a feature vector for searching an image including biometrical data. The term generation function can be configured to generate a feature vector to search for an image of a face. The term generation function can be configured to generate a term for searching data having geospatial attributes. The term generation function can include a Structured Query Language (SQL) processing engine. The term generation function can include a machine learning model trained to generate the set of terms based on the query.
According to one example embodiment of the present disclosure, a system for performing secure analytics using a homomorphic encryption is provided. The system may include at least one processor and a memory storing processor-executable codes, wherein the at least one processor can be configured to implement the operations of the above-mentioned method for performing secure analytics using a homomorphic encryption.
According to yet another example embodiment of the present disclosure, the operations of the above-mentioned method for query processing with adaptive risk decisioning are stored on a machine-readable medium comprising instructions, which when implemented by one or more processors perform the recited method for performing secure analytics using a homomorphic encryption.
Other example embodiments of the disclosure and aspects will become apparent from the following description taken in conjunction with the following drawings.
BRIEF DESCRIPTION OF DRAWINGS
Exemplary embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements.
FIG. 1 shows a block diagram of an example environment suitable for practicing the methods described herein.
FIG. 2 shows a system for homomorphic encryption, according to various embodiments.
FIG. 3 is a block diagram illustrating specifics of homomorphic encryption scheme, according to some example embodiments.
FIG. 4 shows an example scheme of a database including geospatial data associated with usage of mobile devices, according to an example embodiment.
FIG. 5 is an example SQL query executed over the geospatial data, according to an example embodiment.
FIG. 6 is a flow chart of an example method for performing secure analytics using term generation and a homomorphic encryption, according to some example embodiments.
FIG. 7 illustrates an exemplary computer system that may be used to implement some embodiments of the present disclosure.
DETAILED DESCRIPTION
The technology disclosed herein is concerned with methods and systems for query processing. Embodiments of the present disclosure may facilitate processing queries requiring reading data from multiple data sources with different levels of protections.
According to some embodiments of the present disclosure, a system for performing secure analytics using a homomorphic encryption may include at least one server and at least one client. The server can receive, from the client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices. The list of indices can be generated by the client by extracting, using the term generation function, a set of terms from a query mapping, using the hash function, the terms in the set of terms to indices in a list of indices, and encrypting the indices in the list with the public key. The server can apply the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set. The server may extract, using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result. The server can send the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
Referring now to the drawings, various embodiments are described in which like reference numerals represent like parts and assemblies throughout the several views. It should be noted that the reference to various embodiments does not limit the scope of the claims attached hereto. Additionally, any examples outlined in this specification are not intended to be limiting and merely set forth some of the many possible embodiments for the appended claims.
FIG. 1 shows a block diagram of an example environment 100 (also referred to as system 100 ) suitable for practicing the methods described herein. It should be noted, however, that the environment 100 is just one example and is a simplified embodiment provided for illustrative purposes, and reasonable deviations of this embodiment are possible as will be evident for those skilled in the art.
As shown in FIG. 1 , the environment 100 may include one or more clients 110 1 - 110 M , one or more servers 120 1 - 120 N , and communications links 130 . One or more clients 110 1 - 110 M and one or more servers 120 1 - 120 N can be disposed in same and/or different locations (e.g., offices, data centers, cities, counties, geographic regions, countries, continents, and so forth). Additionally or alternatively, one or more clients 110 1 - 110 M and one or more servers 120 1 - 120 N can each be in varied computing environments, including shared computing architectures, hybrid architectures, distinct architectures (e.g., cloud computing environments), and combinations thereof. One or more clients 110 1 - 110 M and one or more servers 120 1 - 120 N can include any of an application-specific integrated circuit (ASIC), field programmable gate array (FPGA), application-specific standard product (ASSP), an electronic circuit, a processor (shared, dedicated, or group) that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality. Additionally or alternatively, one or more clients 110 1 - 110 M and one or more servers 120 1 - 120 N can include at least some of the characteristics of computing systems described further in relation to FIG. 7 .
A target data source may be in a single server or distributed over multiple servers of one or more servers 120 1 - 120 N as target data source 122 1 - 122 N . Target data source 122 1 - 122 N can be unencrypted (in plaintext form), deterministically encrypted (e.g., using Rivest, Shamir, & Adleman (RSA) encryption technology), semantically encrypted (e.g., using Advanced Encryption Standard (AES) technology), and combinations thereof. When target data source 122 1 - 122 N is a combination of encrypted and unencrypted fields, each field can be consistently encrypted or unencrypted. For example, when data source 122 1 - 122 N includes an unencrypted âemployee nameâ field, the employees names are all unencrypted, as opposed to some name names being encrypted and others unencrypted. By way of further non-limiting example, when data source 122 1 - 122 N includes an encrypted âsocial security numberâ field, the social security numbers are all encrypted, as opposed to some social security numbers being encrypted and others unencrypted. Data stored in and/or retrieved from target data source 122 1 - 122 N can be encrypted and/or decrypted as described in relation to FIG. 2 .
Communications links 130 can include various combinations and permutations of wired and wireless networks (e.g., Ethernet, Wi-Fi, Bluetooth, mobile broadband, the Internet, and so forth), internal/external computer busses, and the like, such as described in reference to FIG. 6 . Although depicted as a single âblock,â communications links 130 can be, for example, multiple distinct/separate combinations and permutations of wired and wireless networks, internal/external computer busses, and the like.
In some embodiments, system 100 encrypts a desired query (or analytic) to be executed over target data source 122 1 - 322 N using a homomorphic encryption scheme, such as described with reference to FIG. 2 . For example, system 100 (e.g., one or more clients 110 1 - 110 M ) encrypts the desired query as a homomorphic query matrix (Q_M). Q_M can be encrypted and the desired query should not be recoverable without a private key. For example, the private key can be associated with the keyed hash function. By way of further non-limiting example, the private key is not necessarily associated with the keyed hash function. Since one or more servers 120 1 - 120 N do not decrypt the desired query or the encrypted result E(R), one or more servers 120 1 - 120 N do not have the private key. The Q_M is a construct for conducting homomorphic queries, providing a secure and (completely) encrypted way to perform a query. In contrast, traditional methods of performing queries over data sources require decryption of the query. Construction of Q_M is described in more detail in the U.S. patent application Ser. No. 15/875,914, incorporated herein as reference for all purposes.
FIG. 2 shows system 200 for homomorphic encryption, according to various embodiments. System 200 can include source system 210 , destination system 220 , and communications link 230 . Source system 210 and destination system 220 can include at least some of the characteristics of computing systems described further with reference to FIG. 7 . Source system 210 can include encryption engine 212 . Destination system 220 can include process 224 . Encryption engine 212 and/or process 224 can include any of an application-specific integrated circuit (ASIC), field programmable gate array (FPGA), application-specific standard product (ASSP), an electronic circuit, a processor (shared, dedicated, or group) that executes one or more software or firmware programs, a combinational logic circuit, and/or other suitable components that provide the described functionality.
Encryption engine 212 can encrypt plaintext B into ciphertext Bâ² using a homomorphic encryption algorithm and an encryption key. Homomorphic encryption is a form of encryption in which a certain algebraic operation (generally referred to as addition or multiplication) performed on plaintext is equivalent to another operation performed on ciphertext. Homomorphic encryption algorithms can be partially homomorphic (exhibits either additive or multiplicative homomorphism, or an unlimited number addition or multiplication operations and a limited number of multiplication or addition operations) or fully homomorphic (exhibits both additive and multiplicative homomorphism). For example, in partially homomorphic encryption schemes, multiplication in ciphertext is equal to addition of the same values in plaintext.
Examples of partially homomorphic cryptosystems include RSA (multiplicative homomorphism), ElGamal (multiplicative homomorphism), and Paillier (additive homomorphism). Other partially homomorphic cryptosystems include the Okamoto-Uchiyama, Naccache-Stern, Damgård-Jurik, Sander-Young-Yung, Boneh-Goh-Nissim, and Ishai-Paskin cryptosystems. Examples of fully homomorphic cryptosystems include: the Brakerski-Gentry-Vaikuntanathan, Brakerski's scale-invariant, NTRU-based, and Gentry-Sahai-Waters (GSW) cryptosystems.
Process 224 can be an operation performed (or application which works) on homomorphically encrypted information (e.g., ciphertext Bâ²) such that decrypting the result of the operation is the same as the result of some operation performed on the corresponding plaintext (e.g., plaintext B). For example, a homomorphically encrypted Internet search engine receives encrypted search terms and compare them with an encrypted index of the web. By way of further non-limiting example, a homomorphically encrypted financial database stored in the cloud allows users to ask how much money an employee earned within a particular time period. However, it would accept an encrypted employee name and output an encrypted answer, avoiding the privacy problems that can plague online services that deal with such sensitive data.
Communications link 230 can be various combinations and permutations of wired and wireless networks (e.g., Ethernet, Wi-Fi, Bluetooth, mobile broadband, the Internet, and so forth), internal/external computer busses, and the like, such as described with reference to FIG. 7 .
FIG. 3 is a block diagram showing details of a homomorphic encryption scheme 300 , according to some example embodiments. The scheme 300 can be implemented by the clients 110 1 - 110 M . The clients 110 1 - 110 M can be configured to acquire a desired analytic A to be executed over data sources 122 1 - 122 M .
The scheme 300 may include a term generation (TG) function 310 . The term generation function 310 can be used to extract a set of term elements {T} of analytic A. For example, if the analytic A includes a frequency distribution for database elements in <row:column> pairs where row=Y, then the set {T} reflects the frequency distribution of these elements from the database.
The scheme 300 may further include a keyed hash function H(T) 320 . The keyed hash function H(T) 320 can be used to obtain a set H(T)={H(T): T in {T}}. The set H(T) is the range of the hash function H(T) over the set of term elements {T}. The hash function H(T) can map values of terms to integers in a pre-specified range (for example, from 0 to 2 20 â1). The number of distinct elements in the set H(T) can be more or equal to the number of distinct elements in the set of term elements {T}.
The scheme 300 may further include a homomorphic encryption module 330 . The module 330 can be used to construct a homomorphically encrypted list {E(L)} of indices, wherein E is a homomorphic encryption. The size of the list {E(L)} can be selected to be greater than the number s of distinct elements in the set of term elements {T}. In an example embodiment, the list {E(L)} can be generated using the following rule. For index j=0, . . . , (sâ1): if H(T)=j for a term element T in the set {T}, then position j in the list {E(L)} can be equal to E(B_j), where B_j is a nonzero bit mask corresponding to the term element T and E(B_j) is a result of applying a public key of the homomorphic encryption to B_j. If there is no T in {T} such that H(T)=j, then position j in the list {E(L)} can be equal to E(0). In this manner, the list {E(L)} includes encryptions of nonzero bitmasks for the term elements present in the set {T} only. The analytic A and an unencrypted list {L} of indices of the terms elements {T} cannot be recovered from the list {E(L)} without a private key associated with the homomorphic encryption E. The private key can be kept only on the clients 110 1 - 110 M .
The clients 110 1 - 110 M can send the list of indices {E{L}}, the term generation function TG, the keyed hash function H(T), and the public key of the homomorphic encryption E to the server(s) 120 1 - 120 N .
In some embodiments, the servers 120 1 - 120 N can apply the term generation function TG, the keyed hash function H(T), and the public key of the homomorphic encryption E to data sources 122 1 - 122 N to get a further homomorphically encrypted list of indices and homomorphically encrypted data. The servers 120 1 - 120 N can use the homomorphically encrypted list {E{L}} and the further homomorphically encrypted list of indices to extract data from the homomorphically encrypted data to produce encrypted results E(R). Because the private key of the homomorphic encryption E is kept on the clients 110 1 - 110 M , the servers 120 1 - 120 N cannot decrypt the homomorphically encrypted list {E{L}} or the results E(R). The servers 120 1 - 120 N can send the encrypted result E(R) to the clients 110 1 - 110 M .
The clients 110 1 - 110 M can decrypt the encrypted results E(R) using the private key of the homomorphic encryption E to obtain the results R. Because the list {E(L)} includes nonzero entries for terms in set {T}, the homomorphic properties of the homomorphic encryption E ensure that only results corresponding to the nonzero elements of the list are present in results R.
Thus, the term generation function can be used to determine which fields in data sources are to be read to generate the results of queries or analytics. In various embodiments, the term generation function can be dynamically provided to the clients 110 1 - 110 M at the time of generating a query or analytic. The term generation function can be specified per query basis. This approach may provide flexibility in determination which data (for example, which fields in rows in tables) are to be extracted from the data sources.
In general, the clients 110 1 - 110 N may provide, to the servers 120 1 - 120 N , a homomorphically encrypted list of indices within a fixed range. The fixed range can be numbers starting at zero and ending at, for example, 256, 10 million and so forth. The specific encrypted terms that can be searched for in the data sources can be specified by one of the numbers in the list of indices. The encrypted query can be constructed to look for a particular index (number). The term generation function in combination with keyed hash function can be used as a rule for mapping the terms that to be searched for in the data sources to the indices of the list of indices of the fixed range.
The term generation function may generate terms of external analytics, for example, feature vectors for biometrics, ranges for geospatial query, a phonetic index (for example Soundex), a document search query (for example, Lucene index), terms of SQL requests, and so forth. The term generation function can be a straightforward function configured, for example, can be configured to retrieve a name field from a certain row of data. The term generation function can be a sophisticated function configured to run fields through a trained machine-learning model and output the term.
Example 1
On the client side, the term generation function may include an SQL engine to analyze an SQL query. For example, the SQL query may include a social security number. The term generation function and keyed hash function may be used to determine how to map the social security number to an index in a list of indices of a fixed range. The list of indices can be encrypted using a public key of a homomorphic encryption.
On the server side, the same term generation function, keyed hash function, and the public key of the homomorphic encryption can be applied to the data in data sources, for example tables including records having social security numbers as a field, to determine homomorphically encrypted indices of the same fixed range corresponding to social security numbers found in records of the table. The public key can be applied to all the fields in the records to obtain a homomorphically encrypted table. The homomorphically encrypted list of indices generated on the client side can be then used to indicate which records of the table are to be read from the homomorphically encrypted table to generate a homomorphically encrypted result of the SQL query. For example, a record can be read if a homomorphically encrypted index of social security number in the record matches a homomorphically encrypted index indicated in the list of indices provided by the client. The server cannot decrypt the homomorphically encrypted list of indices or the homomorphically encrypted result without a private key of the homomorphic encryption. The encrypted result can be sent to the client, where the client decrypts the results using a private key.
Example 2
FIG. 4 shows an example scheme 400 of a database storing geospatial data associated with usage of mobile devices, according to an example embodiment. The database of geospatial data can be stored on servers 120 1 - 120 N .
FIG. 5 is an example SQL query 500 to be executed over the geospatial data of the scheme 400 , according to an example embodiment. Specifically, the SQL query 500 requests a list of mobile devices that were within a certain circle of geospatial data.
On the client side, the term generation function can analyze the SQL query to determine a set of terms related to the geospatial data. For example, the term generation function can divide the circle specified in the SQL query 500 into a set of discrete areas that cover the circle. The discrete areas can be of any predetermined shape and size, for example rectangles having certain geospatial coordinates. The discrete areas can be considered as terms of a certain set of terms (set of areas covering a certain area of Earth). The keyed hash function can be then used to map the discrete areas (terms) to indices in a list of indices of a fixed range. The list of indices can be encrypted using a public key of a homomorphic encryption. The client can send the homomorphically encrypted list of indices, the term generation function, the keyed hash function and the public key associated with the homomorphic encryption to the servers.
On the server side, the term generation function, the keyed hash function, and the public key can be applied to records in the database to obtain homomorphically encrypted indices of these records. The fields in the records can also be encrypted with the public key. The server may select the records of homomorphically encrypted indices matching at least one index in the homomorphically encrypted list of indices provided by the client. The selected homomorphically encrypted records may correspond to latitude and longitude points that belong to the circle originally indicated in the SQL query 500 . The server cannot decrypt the selected homomorphically encrypted records because the private key of the homomorphic encryption is kept on the client. The selected homomorphically encrypted records can be decrypted with private key only on the client, so only a user of the client may view a list of mobile devices located in the circle specified by SQL query 500 .
Example 3
On the client side, the term generation function may include a model which generates feature vectors based on, for example, images of faces, images of fingerprints, voice records, and so forth. The feature vectors can be considered as terms belonging to a list of indices of a fixed range, where the list of indices may correspond to all possible feature vectors. For example, all possible feature vectors can be enumerated from zero to a pre-determined maximum number.
If, for example, a query includes an image of a face, then the term generation function may determine, based on the model, the feature vectors corresponding to the face. The keyed hash function can be then used to map the terms (feature vectors) to indices in the list of indices. The list of indices can be encrypted using a public key of a homomorphic encryption. The client can send the homomorphically encrypted list of indices, the term generation function, the keyed hash function and the public key to the servers.
On the server side, the term generation function, the keyed hash function, and the public key can be applied to images stored on the servers to obtain homomorphically encrypted indices of these images. The images can be also encrypted with the public key. The server may select images indices of which match at least one index in the list of indices provided by the client. It should be noted that the matching is performed on homomorphically encrypted indices. The list of selected homomorphically encrypted images can be sent to the client.
Example 4
On the client side, the term generation function can be configured to generate, based one or more keywords, indices for searching text documents including the keywords. For example, the term generation function can generate Lucene indices. The indices for searching text documents can be used as terms belonging to a list of indices of a fixed range. The values of indices for searching text documents can be enumerated from zero to a pre-determined maximum number. The keyed hash function can be then used to map the search indices determined by the term generation function to indices in the list of indices. The list of indices can be encrypted using a public key of a homomorphic encryption. The client can send the homomorphically encrypted list of indices, the term generation function, the keyed hash function and the public key to the servers.
On the server side, the term generation function, the keyed hash function, and the public key can be applied to text documents stored on the servers to obtain indices of these documents. The server may select text documents indices of which match at least one index in the list of indices provided by the client. The matching is performed on the homomorphically encrypted indices. The list of selected text documents can be encrypted with a public key and sent to the client.
Example 5
On the client side, the term generation function may be configured to generate, based on a desired word, phonetic indices for searching a word sound similar to the desired word. For example, the term generation function may generate Soundex indices, which are a coded last name index. The Soundex indices are based on the way a surname sounds rather than the way it is spelled. Soundex indices for last names that sound the same, but are spelled differently, like SMITH and SMYTH, are the same. The phonetic indices are of a fixed range. The keyed hash function can be then used to map the phonetic indices determined by the term generation function to indices in a list of indices of the fixed range. The list of indices can be encrypted using a public key of a homomorphic encryption. The client can send the list of indices, the term generation function, the keyed hash function and the public key to the servers.
On the server side, the term generation function, the keyed hash function, and the public key can be applied to database that store records including the last names to obtain homomorphically encrypted indices of these last names. The last names can be also encrypted with the public key. The server may select records that include homomorphically encrypted last names corresponding to at least one homomorphically encrypted index in the list of indices provided by the client. The list of selected homomorphically encrypted records can be sent to the client.
FIG. 6 is a flow chart of an example method 600 for performing secure analytics using term generation and homomorphic encryption, according to some example embodiments. The method 600 can be performed within environment 100 illustrated in FIG. 1 . Notably, the steps recited below may be implemented in an order different than described and shown in FIG. 6 . Moreover, the method 600 may have additional steps (not shown herein), but which can be evident to those skilled in the art from the present disclosure. The method 600 may also have fewer steps than outlined below and shown in FIG. 6 .
The method 600 may commence in block 605 with receiving, by at least one server, from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices. The homomorphically encrypted list of indexes can be generated by extracting, using the term generation function, a set of terms from a query, mapping, using the hash function, the terms in the set of terms to the indices in the list of indices, and encrypting, using the public key, the indices in the list of indices.
In block 610 , the method 600 may include applying, by the server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set.
In block 615 , the method 600 may include extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result. Extracting data may include matching indices from the further homomorphically encrypted list of indices to indices from the homomorphically encrypted list of indices. The term generation function can be configured to generate at least one search index for searching a document. The term generation function can be configured to generate at least one phonetic index for searching for a word.
The term generation function can be configured to generate a feature vector for searching an image including biometrical data. The term generation function can be configured to generate a feature vector for searching for an image of a face.
The term generation function can be configured to generate a term for searching data having geospatial attributes. The term generation function can include a SQL processing engine. The term generation function includes a machine learning model trained to generate the set of terms based on the query. The term generation function can be dynamically specified by the client at the time of sending the query to the at least one server.
In block 620 , the method 600 may include sending, by the at least one server, the encrypted result to the client. The client can be configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
FIG. 7 illustrates an exemplary computer system 700 that may be used to implement some embodiments of the present disclosure. The computer system 700 of FIG. 7 may be implemented in the contexts of the likes of clients 110 1 - 110 M , servers 120 1 - 120 N , communications links 130 , source system 210 , destination system 220 , and communications link 230 , encryption engine 212 , process 224 , and target data source 122 1 - 122 N . The computer system 700 of FIG. 7 includes one or more processor units 710 and main memory 720 . Main memory 720 stores, in part, instructions and data for execution by processor units 710 . Main memory 720 stores the executable code when in operation, in this example. The computer system 700 of FIG. 7 further includes a mass data storage 730 , portable storage device 740 , output devices 750 , user input devices 760 , a graphics display system 770 , and peripheral devices 780 .
The components shown in FIG. 7 are depicted as being connected via a single bus 790 . The components may be connected through one or more data transport means. Processor unit 710 and main memory 720 is connected via a local microprocessor bus, and the mass data storage 730 , peripheral device(s) 780 , portable storage device 740 , and graphics display system 770 are connected via one or more input/output (I/O) buses.
Mass data storage 730 , which can be implemented with a magnetic disk drive, solid state drive, or an optical disk drive, is a non-volatile storage device for storing data and instructions for use by processor unit 710 . Mass data storage 730 stores the system software for implementing embodiments of the present disclosure for purposes of loading that software into main memory 720 .
Portable storage device 740 operates in conjunction with a portable non-volatile storage medium, such as a flash drive, floppy disk, compact disk, digital video disc, or Universal Serial Bus (USB) storage device, to input and output data and code to and from the computer system 700 of FIG. 7 . The system software for implementing embodiments of the present disclosure is stored on such a portable medium and input to the computer system 700 via the portable storage device 740 .
User input devices 760 can provide a portion of a user interface. User input devices 760 may include one or more microphones, an alphanumeric keypad, such as a keyboard, for inputting alphanumeric and other information, or a pointing device, such as a mouse, a trackball, stylus, or cursor direction keys. User input devices 760 can also include a touchscreen. Additionally, the computer system 700 as shown in FIG. 7 includes output devices 750 . Suitable output devices 750 include speakers, printers, network interfaces, and monitors.
Graphics display system 770 include a liquid crystal display (LCD) or other suitable display device. Graphics display system 770 is configurable to receive textual and graphical information and processes the information for output to the display device.
Peripheral devices 780 may include any type of computer support device to add additional functionality to the computer system.
The components provided in the computer system 700 of FIG. 7 are those typically found in computer systems that may be suitable for use with embodiments of the present disclosure and are intended to represent a broad category of such computer components that are well known in the art. Thus, the computer system 700 of FIG. 7 can be a personal computer (PC), hand held computer system, telephone, mobile computer system, workstation, tablet, phablet, mobile phone, server, minicomputer, mainframe computer, wearable, or any other computer system. The computer may also include different bus configurations, networked platforms, multi-processor platforms, and the like. Various operating systems may be used including UNIX, LINUX, WINDOWS, MAC OS, PALM OS, QNX ANDROID, IOS, CHROME, TIZEN, and other suitable operating systems.
The processing for various embodiments may be implemented in software that is cloud-based. In some embodiments, the computer system 700 is implemented as a cloud-based computing environment, such as a virtual machine operating within a computing cloud. In other embodiments, the computer system 700 may itself include a cloud-based computing environment, where the functionalities of the computer system 700 are
CLAIMS
Claims ( 24 )
What is claimed is:
1. A method for performing secure analytics using a homomorphic encryption, the method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function configured to generate a term for searching data having geospatial attributes, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, a set of terms from a query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function and the hash function to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
2. The method of claim 1 , wherein the extracting includes matching indices from the further homomorphically encrypted list of indices to indices from the homomorphically encrypted list of indices.
3. The method of claim 1 , wherein the term generation function is configured to generate at least one search index for searching a document.
4. The method of claim 1 , wherein the term generation function is configured to generate at least one phonetic index for searching for a word.
5. The method of claim 1 , wherein the term generation function is configured to generate a feature vector for searching an image including biometrical data.
6. The method of claim 5 , wherein the term generation function is configured to generate a feature vector for searching for an image of a face.
7. The method of claim 1 , wherein the term generation function includes a Structured Query Language (SQL) processing engine.
8. The method of claim 1 , wherein the term generation function is dynamically specified by the client at a time of sending the query to the at least one server.
9. The method of claim 1 , wherein the term generation function includes a machine learning model trained to generate the set of terms based on the query.
10. A system for performing secure analytics using a homomorphic encryption, the system comprising:
at least one processor; and
a memory communicatively coupled with the at least one processor, the memory storing instructions, which when executed by the at least processor perform a method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function configured to generate a term for searching data having geospatial attributes, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, a set of terms from a query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
11. The system of claim 10 , wherein the extracting includes matching indices from the further homomorphically encrypted list of indices to indices from the homomorphically encrypted list of indices.
12. The system of claim 10 , wherein the term generation function is configured to generate at least one search index for searching a document.
13. The system of claim 10 , wherein the term generation function is configured to generate at least one phonetic index for searching for a word.
14. The system of claim 10 , wherein the term generation function is configured to generate a feature vector for searching an image including biometrical data.
15. The system of claim 14 , wherein the term generation function is configured to generate a feature vector for searching for an image of a face.
16. The system of claim 10 , wherein the term generation function includes a Structured Query Language (SQL) processing engine.
17. The system of claim 10 , wherein the term generation function is dynamically specified by the client at a time of sending the query to the at least one server.
18. A non-transitory computer-readable storage medium having embodied thereon instructions, which when executed by at least one processor, perform steps of a method, the method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function configured to generate a term for searching data having geospatial attributes, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, a set of terms from a query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function, the hash function, and the public key to a data set to determine a further list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
19. A method for performing secure analytics using a homomorphic encryption, the method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function including a Structured Query Language (SQL) processing engine, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, a set of terms from a query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function and the hash function to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
20. A method for performing secure analytics using a homomorphic encryption, the method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function including a machine learning model trained to generate a set of terms based on a query, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, the set of terms from the query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function and the hash function to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
21. A system for performing secure analytics using a homomorphic encryption, the system comprising:
at least one processor; and
a memory communicatively coupled with the at least one processor, the memory storing instructions, which when executed by the at least processor perform a method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function including a Structured Query Language (SQL) processing engine, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, a set of terms from a query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
22. A system for performing secure analytics using a homomorphic encryption, the system comprising:
at least one processor; and
a memory communicatively coupled with the at least one processor, the memory storing instructions, which when executed by the at least processor perform a method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function including a machine learning model trained to generate a set of terms based on a query, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, the set of terms from the query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function, the hash function, and the public key to a data set to determine a further homomorphically encrypted list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
23. A non-transitory computer-readable storage medium having embodied thereon instructions, which when executed by at least one processor, perform steps of a method, the method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function including a Structured Query Language (SQL) processing engine, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, a set of terms from a query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function, the hash function, and the public key to a data set to determine a further list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
24. A non-transitory computer-readable storage medium having embodied thereon instructions, which when executed by at least one processor, perform steps of a method, the method comprising:
receiving, by at least one server from a client, a term generation function, a hash function, a public key of a homomorphic encryption scheme, and a homomorphically encrypted list of indices, the term generation function including a machine learning model trained to generate a set of terms based on a query, wherein generating the homomorphically encrypted list of indices includes:
extracting, using the term generation function, the set of terms from the query;
mapping, using the hash function, the terms in the set of terms to indices in a list of indices; and
encrypting, using the public key, the indices in the list;
applying, by the at least one server, the term generation function, the hash function, and the public key to a data set to determine a further list of indices and a homomorphically encrypted data set;
extracting, by the at least one server and using the homomorphically encrypted list of indices and the further homomorphically encrypted list of indices, data from the homomorphically encrypted data set to obtain an encrypted result; and
sending, by the at least one server, the encrypted result to the client, wherein the client is configured to decrypt the encrypted result using a private key of the homomorphic encryption scheme.
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Cited By (5)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US20210326470A1
( en )
*
2020-04-17
2021-10-21
Matthew Raymond Fleck
Data sundering
US11902413B2
( en )
2017-01-20
2024-02-13
Enveil, Inc.
Secure machine learning analytics using homomorphic encryption
US20240104224A1
( en )
*
2022-09-27
2024-03-28
Intel Corporation
Privacy-preserving search using homomorphic encryption
US12200102B1
( en )
*
2024-06-26
2025-01-14
Atombeam Technologies Inc
Codebook-based homomorphic encryption for efficient and privacy-preserving data processing
US12542650B2
( en )
2023-10-03
2026-02-03
Bank Of America Corporation
Artificial intelligence (AI) based cloud architecture segmentation leveraging homomorphic encryption
Families Citing this family (11)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US11507683B2
( en )
2017-01-20
2022-11-22
Enveil, Inc.
Query processing with adaptive risk decisioning
US10873568B2
( en )
2017-01-20
2020-12-22
Enveil, Inc.
Secure analytics using homomorphic and injective format-preserving encryption and an encrypted analytics matrix
US10903976B2
( en )
2017-01-20
2021-01-26
Enveil, Inc.
End-to-end secure operations using a query matrix
US10972251B2
( en )
2017-01-20
2021-04-06
Enveil, Inc.
Secure web browsing via homomorphic encryption
US10902133B2
( en )
2018-10-25
2021-01-26
Enveil, Inc.
Computational operations in enclave computing environments
US11601258B2
( en )
2020-10-08
2023-03-07
Enveil, Inc.
Selector derived encryption systems and methods
KR102642034B1
( en )
*
2021-11-09
2024-02-28
주ìíì¬ ëì¬ì¼ë¡
Method and system for providing privacy-preserving data analysis
US11496288B1
( en )
*
2022-04-08
2022-11-08
Verkada Inc.
Enhanced encryption for face-related data
EP4505659A4
( en )
*
2022-04-08
2025-07-30
Verkada Inc
ENHANCED ENCRYPTION FOR FACIAL DATA
CN115470506B
( en )
*
2022-10-28
2023-03-10
å±±ä¸å翼微çµåææ¯è¡ä»½æéå ¬å¸
Homomorphic mapping-based secure file system implementation method
US20240187848A1
( en )
*
2022-12-06
2024-06-06
VMware LLC
Wan optimization for encrypted data traffic using fully homomorphic encryption
Citations (174)
* Cited by examiner, â Cited by third party
Publication number
Priority date
Publication date
Assignee
Title
US5732390A
( en )
1993-06-29
1998-03-24
Sony Corp
Speech signal transmitting and receiving apparatus with noise sensitive volume control
US6178435B1
( en )
1998-06-30
2001-01-23
International Business Machines Corporation
Method and system for performing a power of two estimation within a data processing system
US20020032712A1
( en )
1998-02-04
2002-03-14
Shuji Miyasaka
Exponential calculation device and decoding device
US20020073316A1
( en )
1998-02-03
2002-06-13
Thomas Collins
Cryptographic system enabling ownership of a secure process
US20020104002A1
( en )
2001-01-26
2002-08-01
Itaru Nishizawa
Database access method and system capable of concealing the contents of query
US20030037087A1
( en )
2001-08-14
2003-02-20
Sun Microsystems, Inc.
Apparatus and method for efficient modular exponentiation
US20030059041A1
( en )
2001-06-26
2003-03-27
Mackenzie Philip D.
Methods and apparatus for two-party generation of DSA signatures
US20030110388A1
( en )
1996-12-04
2003-06-12
Rainbow Technologies, Inc.
Software protection device and method
US6745220B1
( en )
2000-11-21
2004-06-01
Matsushita Electric Industrial Co., Ltd.
Efficient exponentiation method and apparatus
US6748412B2
( en )
2001-09-26
2004-06-08
Intel Corporation
Square-and-multiply exponent processor
US20040167952A1
( en )
2001-06-21
2004-08-26
Shay Gueron
Method and apparatus for carrying out efficiently arithmetic computations in hardware
US20050008152A1
( en )
2003-06-20
2005-01-13
Mackenzie Philip D.
Methods and apparatus for providing secure two-party public key cryptosystem
US20050076024A1
( en )
2003-10-06
2005-04-07
International Business Machines Corporation
System, method and program for database searching
US6910059B2
( en )
2002-07-09
2005-06-21
Silicon Integrated Systems Corp.
Apparatus and method for calculating an exponential calculating result of a floating-point number
US20050259817A1
( en )
2004-05-20
2005-11-24
Ramzan Zulfikar A
Method and apparatus for communication efficient private information retrieval and oblivious transfer
US20060008080A1
( en )
2004-07-09
2006-01-12
Nec Electronics Corporation
Modular-multiplication computing unit and information processing unit
US20060008081A1
( en )
2004-07-09
2006-01-12
Nec Electronics Corporation
Modular-multiplication computing unit and information-processing unit
US20070053507A1
( en )
2005-09-02
2007-03-08
Paris Smaragdis
Secure classifying of data with Gaussian distributions
US20070095909A1
( en )
2002-02-20
2007-05-03
David Chaum
Ballot integrity systems
US20070143280A1
( en )
2005-12-19
2007-06-21
Microsoft Corporation
Determining cardinality of a parameter using hash values
US20070140479A1
( en )
2005-12-19
2007-06-21
Microsoft Corporation
Privacy-preserving data aggregation using homomorphic encryption
US20090037504A1
( en )
2007-08-02
2009-02-05
Via Technologies, Inc.
Exponent Processing Systems and Methods
US20090083546A1
( en )
2004-10-19
2009-03-26
Palo Alto Research Center Incorporated
System And Method For Providing Private Inference Control
US20090193033A1
( en )
2005-05-19
2009-07-30
Zulfikar Amin Ramzan
Method and apparatus for communication efficient private information retrieval and oblivious transfer
US20090268908A1
( en )
2008-04-29
2009-10-29
Daniel Martin Bikel
Methods and Apparatus for Securely Classifying Data
US20090279694A1
( en )
2008-05-09
2009-11-12
Kenta Takahashi
Privacy-preserving scalar product calculation system, privacy-preserving scalar product calculation method and cryptographic key sharing system
US20090287837A1
( en )
2000-07-06
2009-11-19
David Paul Felsher
Information record infrastructure, system and method
US7712143B2
( en )
2006-09-27
2010-05-04
Blue Ridge Networks, Inc.
Trusted enclave for a computer system
US20100202606A1
( en )
2007-01-05
2010-08-12
Unoweb Inc.
Two strings private key (symmetric) encryption and decryption method
US20100205430A1
( en )
2009-02-06
2010-08-12
Shin-Yan Chiou
Network Reputation System And Its Controlling Method Thereof
US20100241595A1
( en )
2000-07-06
2010-09-23
David Paul Felsher
Information record infrastructure, system and method
US7849185B1
( en )
2006-01-10
2010-12-07
Raytheon Company
System and method for attacker attribution in a network security system
US7870398B2
( en )
2007-01-25
2011-01-11
International Business Machines Corporation
Integrity assurance of query result from database service provider
US20110026781A1
( en )
2009-07-02
2011-02-03
Carmel-Haifa University Economic Corporation Ltd.
System for secure face identification (scifi) and methods useful in conjunction therewith
US7937270B2
( en )
2007-01-16
2011-05-03
Mitsubishi Electric Research Laboratories, Inc.
System and method for recognizing speech securely using a secure multi-party computation protocol
US20110107105A1
( en )
2009-10-30
2011-05-05
International Business Machines Corporation
Message sending/receiving method
US20110110525A1
( en )
2009-11-10
2011-05-12
International Business Machines Corporation
Fully homomorphic encryption method based on a bootstrappable encryption scheme, computer program and apparatus
US20110243320A1
( en )
2010-03-30
2011-10-06
International Business Machines Corporation
Efficient Homomorphic Encryption Scheme For Bilinear Forms
US20110283099A1
( en )
2010-05-13
2011-11-17
Microsoft Corporation
Private Aggregation of Distributed Time-Series Data
US20120039469A1
( en )
2006-10-17
2012-02-16
Clay Von Mueller
System and method for variable length encryption
US20120054485A1
( en )
2010-08-25
2012-03-01
Sony Corporation
Terminal device, server, data processing system, data processing method, and program
US20120066510A1
( en )
2010-09-15
2012-03-15
At&T Intellectual Property I, L.P.
Methods, systems, and computer program products for performing homomorphic encryption and decryption on individual operations
US20120201378A1
( en )
2011-02-03
2012-08-09
Mohamed Nabeel
Efficient, remote, private tree-based classification using cryptographic techniques
US20120265797A1
( en )
2011-04-18
2012-10-18
Inside Secure
Montgomery multiplication method
US20120265794A1
( en )
2011-04-18
2012-10-18
Inside Secure
Montgomery multiplication circuit
US20130010950A1
( en )
2011-07-08
2013-01-10
Sap Ag
Public-Key Encrypted Bloom Filters With Applications To Private Set Intersection
US20130054665A1
( en )
2011-08-26
2013-02-28
Cognitive Electronics, Inc.
Methods and systems for performing exponentiation in a parallel processing environment
US20130051551A1
( en )
2011-08-29
2013-02-28
Laila EL AIMANI
Signcryption method and device and corresponding signcryption verification method and device
US20130114811A1
( en )
2011-11-08
2013-05-09
Petros T. Boufounos
Method for Privacy Preserving Hashing of Signals with Binary Embeddings
US20130148868A1
( en )
2009-09-04
2013-06-13
Gradiant
System for secure image recognition
US20130170640A1
( en )
2011-04-29
2013-07-04
International Business Machines Corporation
Fully Homomorphic Encryption
US20130191650A1
( en )
2012-01-25
2013-07-25
Massachusetts Institute Of Technology
Methods and apparatus for securing a database
US20130195267A1
( en )
2010-10-08
2013-08-01
Thomson Licensing
Public key encryption system based on the quadratic residuosity assumption
US20130198526A1
( en )
2006-02-22
2013-08-01
Fujitsu Semiconductor Limited
Secure processor
US8515058B1
( en )
2009-11-10
2013-08-20
The Board Of Trustees Of The Leland Stanford Junior University
Bootstrappable homomorphic encryption method, computer program and apparatus
US20130216044A1
( en )
2012-02-17
2013-08-22
International Business Machines Corporation
Homomorphic evaluation including key switching, modulus switching, and dynamic noise management
US20130230168A1
( en )
2010-11-15
2013-09-05
Nec Corporation
Information processing device, information processing method, and computer readable medium
US20130237242A1
( en )
2012-03-09
2013-09-12
Research In Motion Limited
Location identification of a portable electronic device based on characteristics of an operating environment of the portable electronic device
US20130246813A1
( en )
2011-11-11
2013-09-19
Nec Corporation
Database encryption system, method, and program
US8565435B2
( en )
2010-08-16
2013-10-22
International Business Machines Corporation
Efficient implementation of fully homomorphic encryption
US20130318351A1
( en )
*
2011-02-22
2013-11-28
Mitsubishi Electric Corporation
Similarity degree calculation system, similarity degree calculation apparatus, computer program, and similarity degree calculation method
US20130326224A1
( en )
2012-05-29
2013-12-05
Robert Bosch Gmbh
System and Method for Message Verification in Broadcast and Multicast Networks
US20130339751A1
( en )
2012-06-15
2013-12-19
Wei Sun
Method for Querying Data in Privacy Preserving Manner Using Attributes
US20130339722A1
( en )
2011-11-07
2013-12-19
Parallels IP Holdings GmbH
Method for protecting data used in cloud computing with homomorphic encryption
US20130346755A1
( en )
2012-06-21
2013-12-26
Microsoft Corporation
Homomorphic Signatures and Network Coding Signatures
US20130346741A1
( en )
2012-06-21
2013-12-26
Korea Basic Science Institute
Method for authenticating low performance device
KR101386294B1
( en )
2010-04-27
2014-04-17
ëí°ë´ì ë ì ê°ë¶ìí¤ê°ì´ì¤
Encryption processing system, key generation device, encryption device, decryption device, signature processing system, signature device and verification device
US20140164758A1
( en )
2012-12-07
2014-06-12
Microsoft Corporation
Secure cloud database platform
WO2014105160A1
( en )
2012-12-28
2014-07-03
Intel Corporation
Logging in secure enclaves
US20140189811A1
( en )
2012-12-29
2014-07-03
Zane M. Taylor
Security enclave device to extend a virtual secure processing environment to a client device
US8781967B2
( en )
2005-07-07
2014-07-15
Verance Corporation
Watermarking in an encrypted domain
US20140233727A1
( en )
2012-11-16
2014-08-21
Raytheon Bbn Technologies Corp.
Method for secure substring search
US8832465B2
( en )
2012-09-25
2014-09-09
Apple Inc.
Security enclave processor for a system on a chip
US20140281511A1
( en )
2013-03-15
2014-09-18
Microsoft Corporation
Secure data processing on sensitive data using trusted hardware
US20140355756A1
( en )
2011-09-14
2014-12-04
National Institute Of Advanced Industrial Science And Technology
Search system, search method, and program
JP5680007B2
( en )
2012-03-06
2015-03-04
ä¸è±é»æ©æ ªå¼ä¼ç¤¾
Cryptographic system, cryptographic method and cryptographic program
US20150100794A1
( en )
2013-10-08
2015-04-09
Thomson Licensing
Method for signing a set of binary elements, and updating such signature, corresponding electronic devices and computer program products
US20150100785A1
( en )
2013-10-09
2015-04-09
Thomson Licensing
Method for ciphering a message via a keyed homomorphic encryption function, corresponding electronic device and computer program product
US9059855B2
( en )
2010-12-22
2015-06-16
Intel Corporation
System and method for implementing a trusted dynamic launch and trusted platform module (TPM) using secure enclaves
EP2887607A1
( en )
2013-12-23
2015-06-24
Orange
Migration of assets of a trusted execution environment
WO2015094261A1
( en )
2013-12-19
2015-06-25
Intel Corporation
Policy-based trusted inspection of rights managed content
US20150205967A1
( en )
2014-01-23
2015-07-23
Abb Technology Ag
Method for processing data streams with multiple tenants
US9094378B1
( en )
2013-08-16
2015-07-28
Google Inc.
Homomorphic cryptography on numerical values in digital computing
<a href="/patent/US20150215123A1/en"