Compression and homomorphic encryption in secure query and analytics

Information

  • Patent Grant
  • 11290252
  • Patent Number
    11,290,252
  • Date Filed
    Friday, January 19, 2018
    6 years ago
  • Date Issued
    Tuesday, March 29, 2022
    2 years ago
  • Inventors
  • Original Assignees
  • Examiners
    • Pwu; Jeffrey C
    • Anderson; Michael D
    Agents
    • Carr & Ferrell LLP
Abstract
Systems and methods for end-to-end encryption and compression are described herein. A query is encrypted at a client using a homomorphic encryption scheme. The encrypted query is sent to a server where the encrypted query is evaluated over target data to generate encrypted response without decrypting the encrypted query. The result elements of the encrypted response are grouped, co-located, and compressed, without decrypting the encrypted query or the encrypted response. The compressed encrypted response is sent to the client where it is decrypted and decompressed to obtain the results of the query without revealing the query or results to the owner of the target data, an observer, or an attacker.
Description
FIELD OF THE PRESENT TECHNOLOGY

The present disclosure relates to the technical field of encryption and decryption methods and apparatus as applied to computing systems. More particularly, the present invention is in the technical field of homomorphic encryption methods and apparatus.


SUMMARY

The present invention is a method for compressing results (both intermediate and final) within an end-to-end secure query or analytic system that uses homomorphic encryption.


Various embodiments of the present technology include a method of receiving at one or more servers an encrypted analytic from one or more clients, the analytic encrypted using a homomorphic encryption scheme; evaluating the encrypted analytic over a target data source without decrypting the encrypted analytic; grouping similar result elements of encrypted analytic evaluation based on a probability that the result elements are similar; co-locating two or more groups of result elements on the one or more servers based on a probability that the result elements are similar; converting the grouped result elements into byte streams; compressing the groups of similar result elements; evaluating the encrypted analytic over each of the compressed groups of result elements to generate an encrypted response, without decrypting the encrypted response and without decrypting the encrypted analytic; and sending the encrypted response from the one or more servers to the one or more clients for decompression and decryption at the one or more clients.


Various embodiments of the present technology include a system including a client configured to encrypt an analytic using a homomorphic encryption scheme and an encryption key, send the encrypted analytic to a server without the encryption key, and decrypt an encrypted response using the homomorphic encryption scheme and the key, and decompress the response after decrypting the encrypted response. The system further includes a server configured to receive the encrypted analytic without the encryption key from the client via a network, evaluate the encrypted analytic over a target data source without decrypting the encrypted analytic, group similar result elements of the target data source evaluation based on a probability that the result elements are similar, co-locate similar result elements from another server based on a probability that the result elements are similar, convert the result elements into byte streams, compress the groups of similar result elements, evaluate the encrypted analytic over the compressed groups of result elements to generate an encrypted response, without decrypting the encrypted analytic and without decrypting the encrypted response, and send the encrypted response to the client.


Various embodiments of the present technology include a non-transitory computer readable storage media having a program embodied thereon, the program being executable by a processor to perform a method for secure analytics of a target data source, the method comprising: receiving an encrypted analytic from a client via a network, the analytic encrypted using a homomorphic encryption scheme and a public encryption key, the encrypted analytic received without the corresponding private encryption key; evaluating the encrypted analytic over the target data source to generate encrypted result elements without decrypting the encrypted analytic; grouping similar result elements of the encrypted analytic evaluation based on a probability that the result elements are similar without decrypting the result elements; co-locating two or more groups of result elements on the server based on a probability that the result elements are similar; converting the grouped result elements into byte streams; compressing the groups of similar result elements; evaluating the encrypted analytic over the compressed groups of result elements to generate an encrypted response, without decrypting the encrypted analytic and without decrypting the encrypted response; and sending the encrypted response to the client.





BRIEF DESCRIPTION OF THE DRAWINGS

Certain embodiments of the present technology are illustrated by the accompanying figures. It will be understood that the figures are not necessarily to scale and that details not necessary for an understanding of the technology or that render other details difficult to perceive may be omitted. It will be understood that the technology is not necessarily limited to the particular embodiments illustrated herein.



FIG. 1 illustrates an exemplary end-to-end encryption system, in accordance with aspects of the claimed technology.



FIG. 2 illustrates details of a client of FIG. 1.



FIG. 3 illustrates details of a server of FIG. 1.



FIG. 4 is a flowchart illustrating an exemplary method for end-to-end encryption, in accordance with various aspects of the technology.



FIG. 5 is a schematic diagram of an exemplary computing system that is used to implement embodiments according to the present technology.





DETAILED DESCRIPTION

Homomorphic encryption is a form of encryption in which a specific algebraic operation (generally referred to as addition or multiplication) performed on data is equivalent to another operation performed on the encrypted form of data. For example, in Partially Homomorphic Encryption (PHE) schemes, multiplication performed on data such as ciphertext is equal to addition of the same values in plaintext. Thus, a specific operation performed on homomorphically encrypted data (e.g., an analytic) may generate an encrypted result which, when decrypted, allows recovery of the result of the operation as if it had been performed on the unencrypted data. For example, a homomorphically encrypted analytic such as a query may be evaluated using target data to generate an encrypted response. The encrypted response may be decrypted, and the decrypted response may be used to recover the evaluation of the query as if it had been evaluated over the target data using the unencrypted query.


Homomorphic encryption can also be used to securely chain together multiple operations on homomorphically encrypted data without exposing unencrypted data. The result of the multiple chained operations can then be recovered as if the multiple operations had been performed on the unencrypted data. It is noteworthy that if one of those multiple operations is a compression, the uncompressed data may be recovered as if the compression had been performed on unencrypted data. For example, a homomorphically encrypted query may be evaluated over target data and the query response then compressed. The result of the query evaluation may be recovered using decryption and extraction as if the query evaluation and compression had been performed on an unencrypted query.



FIG. 1 illustrates an exemplary end-to-end encryption system 100 in accordance with aspects of the claimed technology. The encryption system 100 may provide for end-to-end secure analytics, such as queries, using homomorphic encryption utilizing compression techniques. The encryption system 100 includes a client 102 and one or more servers 110 that include data 112. FIG. 1 illustrates one client 102 and three servers 110 that include data 112. However, the system 100 may comprise more than one client 102. Also, the system 100 may comprise more servers 110 or fewer servers 110 that include data 112. In some embodiments, a plurality of clients 102 communicate encrypted analytics to one server 110 and receive encrypted responses from the server 110. In some embodiments, one client 102 communicates encrypted analytics to a plurality of servers 110 and receives encrypted responses from the plurality of servers 110. In some embodiments, one client 102 communicates encrypted analytics to one server 110 and receives encrypted responses from the server 110. In some embodiments, a plurality of clients 102 communicate encrypted analytics to a plurality of servers 110 and receive encrypted responses from the plurality of servers 110. In general, one or more clients 102 communicate encrypted analytics to, and/or receive encrypted responses from, one or more of servers 110. While in general, one or more clients 102 and one or more servers 110 is contemplated, where for simplicity the case of one client 102 and multiple servers 110 is illustrated elsewhere herein, it is no way intended to limit the practice of the claimed technology to a single client 102 and/or a plurality of servers 110. Thus, various descriptions of modules, acts, communications, and/or acts that are illustrated in the context of one client 102 for simplicity and clarity, may apply to multiple clients 102 and vice versa. Similarly, various descriptions of modules, acts, communications, and/or acts that are illustrated in the context of one server 110 for simplicity and clarity, may apply to multiple servers 110 and vice versa.


The client 102 and servers 110 of FIG. 1 may communicate via a network 122. In various embodiments, the network 122 is various combinations and permutations of wired and wireless networks (e.g., Ethernet, Wi-Fi, Bluetooth, mobile broadband, the Internet, etc.), internal/external computer busses, and/or the like. In some embodiments, one or more clients 102 communicate directly with one or more servers 110.


There may be multiple servers 110 having data 112 that are available for access from the client 102, as illustrated in FIG. 1. A target data source D may reside in data 112 on a single server 110 or may be distributed over data 112 in multiple servers 110 in the encryption system 100, in a plurality of distinct locations, which could include different blades in a server system, containers in a cloud, or servers that are geographically remote from one another, just as examples. Thus, the target data source D could be partly stored on the data 112, partly on a cloud (not illustrated), or the data source could be wholly stored on either. In various embodiments, the target data source distributed over one or more data 112 is unencrypted (in plaintext form), deterministically encrypted, semantically encrypted, and/or other similar formats that would be known to one of ordinary skill in the art with the present disclosure before them, or any combination thereof.



FIG. 1 illustrates three servers 110 that include data 112. However system 100 may comprise more servers 110 or fewer servers 110. While a single client 102 is illustrated in the system 100 of FIG. 1, in general the system 100 may comprise multiple clients 102 or a client 102 that is implemented on multiple systems and/or locations. In some embodiments, one or more clients 102 communicate encrypted analytics to, and/or receive encrypted responses from, one or more servers 110. In various embodiments, the server(s) 110 and the client(s) 102 are implemented in varied computing environments, including shared computing architectures, hybrid architectures, or distinct architectures such as those in a cloud computing environment.



FIG. 2 illustrates details of the client 102 of FIG. 1. The client 102 includes a homomorphic encryption module 202, a homomorphic decryption module 204, and an encryption key 206. The homomorphic encryption module 202, homomorphic decryption module 204, and an encryption key 206 may be associated with a homomorphic encryption scheme E, such as Paillier encryption, or any other homomorphic encryption. The homomorphic encryption scheme E may be a fully or partially homomorphic encryption scheme. 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.



FIG. 2 illustrates a single encryption key 206. However, in some embodiments the encryption key 206 includes a public key and a private key. FIG. 2 further includes a decompression module 208 for extracting data from compressed data according to various decompression techniques T.


An analytic (e.g., a query Q) may be evaluated using data within the target data source D. Using the homomorphic encryption scheme E, the encryption system 100 may encode the query Q as a homomorphically encrypted query Q_E using the homomorphic encryption module 202 and encryption key 206. In various embodiments, the query Q_E is encoded as a homomorphic query vector, a homomorphic query matrix, homomorphic query parameters, and/or the like. The encrypted query Q_E is completely encrypted. The query Q cannot be recovered from encrypted query Q_E without using the key 206, which is associated with encryption scheme E. The homomorphic decryption module 204 is configured to use the encryption key 206 to evaluate an operation K{Q_E, E}, which decrypts the encrypted query Q_E using the encryption scheme E and key 206.


The client 102 may send the encrypted query Q_E from the client 102 to one or more servers 110 containing the target data source in data 112. However, the client 102 does not send the key 206 to any of the servers 110. Thus, servers 110 are not able to recover the encrypted query Q_E, without the key 206.



FIG. 3 illustrates details of the server 110 of FIG. 1. In addition to the data 112, the server 110 includes, the encrypted query (encrypted query 302) received from the client 102, an evaluation module 304, an element grouping module 306, and an element compression module 308 for compressing data according to various decompression techniques T. The encrypted query 302 may be received from the client without the key 206. Thus, the server 110 is unable to recover or expose the query Q, because the server 110 is unable decrypt the encrypted query 302 without the key 206. The evaluation of a homomorphically encrypted analytic (e.g., encrypted query 302) over data may produce an encrypted response that may be recovered using the key 206.


Using techniques of the homomorphic encryption scheme E, one or more server 110 evaluates the encrypted query Q_E 302 over target data D, which resides within the one or more of, respective, data 112. The evaluation may produce an encrypted response E(R).


As the evaluation module 304 evaluates the encrypted query Q_E 302 over the target data D, the element grouping module 306 is configured to group the most probable similar result elements of the target data D. The element grouping module 306 may co-locate the similar result elements of a group on the same computing device, e.g., at the same server 110. The element grouping module 306 may convert the similar result elements into byte streams. The element compression module 308 compresses the byte streams of result elements using one or more compression techniques T. The evaluation module 304 then evaluates the encrypted query Q_E 302 over each group of the compressed data elements, producing a compressed, encrypted response E(R). The compression of the grouped data elements reduces the size of the encrypted response E(R). The compression of the grouped data elements also reduces the amount of computation that needs to be performed by the evaluation module 304 to evaluate the encrypted query Q_E 302 over the groups of compressed data elements and produce the compressed encrypted response E(R).


The operations of grouping the elements, converting grouped elements into byte streams, compressing the grouped byte streams and evaluating the encrypted query Q_E 302 are each performed without decrypting the query Q_E 302 at the server 110, and without revealing the unencrypted query Q to the owner of the data 112, an observer, or an attacker. This is because key 206 is not available at any of the one or more servers 110. Further, the operations of grouping the elements, converting grouped elements into byte streams, compressing the grouped byte streams and evaluating the encrypted query Q_E 302 are each performed without decrypting the encrypted response E(R), or revealing the contents of the encrypted response E(R) to the owner of the data 112, an observer, or an attacker. This is also because key 206 is not available at any of the one or more servers 110.


The server 110 sends the compressed encrypted response E(R) to the client 102. Using the key 206 associated with encrypted query Q_E 302, the homomorphic decryption module 204 of the client 102 may apply the operation K{E(R), E} to decrypt the encrypted response E(R). The decompression module 208 may use the decompression techniques associated with T, to decompresses the decrypted results and obtain the results R of the query Q. In some embodiments, the decompression module 208 uses the decompression techniques associated with T, to extract the encrypted response E(R), and the homomorphic decryption module 204 decrypts the decompressed results to obtain the results R of the query Q.


In general, a server (e.g., server(s) 110) comprises one or more programs that share their resources with clients (e.g., client 102). Server programs may be implemented on one or more computers. A client may request content from a server or may request the server to perform a service function while not sharing any of the client's resources. Whether a computer is a client, a server, or both, is determined by the nature of the application that requires the service functions.


While a single client 102 is illustrated in the system 100 of FIG. 1, the system 100 may comprise multiple clients 102 that share the key 206. For example, a first client 102 may encrypt an analytic using the homomorphic encryption scheme E. The encrypted analytic may be sent to one or more servers 110 with directions to return the encrypted response E(R) to a second client 102, where the response may be decrypted and decompressed to obtain the response R. In general, one or more clients 102 communicate encrypted analytics to, and/or receive encrypted responses from, one or more servers 110


In some embodiments, the client 102 and/or servers 110 may implement an application programming interface (API) to formalize data exchange. Both client 102 and server 110 may reside in the same system, and client software may communicate with server software within the same computer.


In some instances, the functions of the client 102 and/or servers 110 are implemented within a cloud-based computing environment, not illustrated. The client 102 and/or servers 110 may be communicatively coupled directly or via the network 122 with a cloud based computing environment. In general, a cloud-based computing environment is an internet resource that typically combines the computational power of a large model of processors and/or that combines the storage capacity of a large model of computer memories or storage devices. For example, systems that provide a cloud resource may be utilized exclusively by their owners; or such systems may be accessible to outside users who deploy applications within the computing infrastructure to obtain the benefit of large computational or storage resources.



FIG. 4 is a flowchart of an exemplary method 400 for end-to-end encryption, in accordance with various aspects of the technology. The method comprises a step 402 of encrypting an analytic. In various embodiments, the analytic is a query, database access, database search, model, classifier, and/or the like. The analytic may be encrypted at one or more client(s) 102 using a homomorphic encryption scheme. The encrypted analytic (referred to elsewhere herein as Q_E 302) may be sent from the client 102 to the server 110 without the encryption key, e.g. encryption key 206. The method 400 comprises a step 404 of receiving the encrypted analytic from the client 102. The encrypted analytic may be received via a network, e.g., the Internet, at one or more servers 110.


The method 400 further comprises a step 406 of evaluating the encrypted analytic over a target data source to generate result elements. The target data source may reside in data 112 at one or more servers 110. The encrypted analytic may be evaluated without decrypting the encrypted analytic and without exposing the unencrypted analytic at the server 110 to the owner of the data 112, an observer, or an attacker. The method 400 further comprises a step 408 of grouping similar result elements. The method 400 also comprises a step 410 of co-locating similar result elements of a group on the same server. The grouping and co-location of the result elements may be based on a probability that the result elements are similar. The result elements may be grouped and co-located without decrypting any of the result elements. The method 400 also comprises a step 412 of converting similar result elements to byte streams. The result elements may be converted to byte streams without decrypting the byte streams.


The method 400 further comprises a step 414 of compressing groups of similar result elements. The groups may be compressed without decrypting the encrypted analytic and without decrypting the encrypted result elements in the groups. The method then comprises a step 416 of evaluating the encrypted analytic over each group of compressed result elements to generate an encrypted response. The encrypted analytic may be evaluated over each group without decrypting the encrypted analytic and without decrypting the encrypted response.


The method 400 further comprises a step 418 of sending the encrypted response to the client 102. The encrypted response may be sent from one or more servers 110 to the client 102 via the network 122. The method 400 also comprises a step 420 of decrypting the encrypted response received from the one or more servers 110 at the client 102. The method also comprises a step 422 of decompressing the decrypted response at the client 102.


Thus, using the method 400, the analytic may be evaluated over the target data source in a completely secure and private manner. Moreover, neither the contents nor the results of the analytic are revealed by the method 400 to the owner of the target data source, an observer, or an attacker.



FIG. 5 is a diagrammatic representation of an example machine in the form of a computer system 500, within which a set of instructions for causing the machine to perform any of one or more of the methodologies discussed herein may be executed. In various example embodiments, the machine operates as a standalone device or may be connected (e.g., networked) to other machines. In a networked deployment, the machine may operate in the capacity of a server (e.g. server 110) or a client (e.g., client 102) machine, in a server-client network environment, or as a peer machine in a peer-to-peer (or distributed) network environment. The machine may be a personal computer (PC), a tablet PC, a set-top box (STB), a personal digital assistant (PDA), a cellular telephone, a portable music player (e.g., a portable hard drive audio device such as an Moving Picture Experts Group Audio Layer 3 (MP3) player), a web appliance, a network router, switch or bridge, or any machine capable of executing a set of instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, while only a single machine is illustrated in FIG. 5, the term “machine” shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein


The example computer system 500 includes a processor or multiple processor(s) 502 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), or both), and a main memory 506 and static memory 508, which communicate with each other via a bus 522. The computer system 500 may further include a video display 512 (e.g., a liquid crystal display (LCD)). The computer system 500 may also include an alpha-numeric input device(s) 514 (e.g., a keyboard), a cursor control device (e.g., a mouse, trackball, touchpad, touch screen, etc.), a voice recognition or biometric verification unit (not shown), a drive unit 516 (also referred to as disk drive unit), a signal generation device 520 (e.g., a speaker), and a network interface device 510. The computer system 500 may further include a data encryption module (shown elsewhere herein) to encrypt data.


The disk drive unit 516 includes a computer or machine-readable medium 518 on which is stored one or more sets of instructions and data structures (e.g., instructions 504) embodying or utilizing any one or more of the methodologies or functions described herein. The instructions 504 may also reside, completely or at least partially, within the main memory 506 and/or within the processor(s) 502 during execution thereof by the computer system 500. The main memory 506 and the processor(s) 502 may also constitute machine-readable media.


The instructions 504 may further be transmitted or received over a network (e.g., network 122, see also FIG. 1) via the network interface device 510 utilizing any one of a number of well-known transfer protocols (e.g., Hyper Text Transfer Protocol (HTTP)). While the machine-readable medium 518 is shown in an example embodiment to be a single medium, the term “computer-readable medium” should be taken to include a single medium or multiple media (e.g., a centralized or distributed database and/or associated caches and servers) that store the one or more sets of instructions. The term “computer-readable medium” shall also be taken to include any medium that is capable of storing, encoding, or carrying a set of instructions for execution by the machine and that causes the machine to perform any one or more of the methodologies of the present application, or that is capable of storing, encoding, or carrying data structures utilized by or associated with such a set of instructions. The term “computer-readable medium” shall accordingly be taken to include, but not be limited to, solid-state memories, optical and magnetic media, and carrier wave signals. Such media may also include, without limitation, hard disks, floppy disks, flash memory cards, digital video disks, random access memory (RAM), read only memory (ROM), and/or the like. The example embodiments described herein may be implemented in an operating environment comprising software installed on a computer, in hardware, or in a combination of software and hardware.


The corresponding structures, materials, acts, and equivalents of any means or step plus function elements in the claims below are intended to include any structure, material, or act for performing the function in combination with other claimed elements as specifically claimed. The description of the present technology has been presented for purposes of illustration and description, but is not intended to be exhaustive or limited to the present technology in the form disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the present technology. Exemplary embodiments were chosen and described in order to best explain the principles of the present technology and its practical application, and to enable others of ordinary skill in the art to understand the present technology for various embodiments with various modifications as are suited to the particular use contemplated.


Aspects of the present technology are described above with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the present technology. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.


These computer program instructions may also be stored in a computer readable medium that can direct a computer, other programmable data processing apparatus, or other devices to function in a particular manner, such that the instructions stored in the computer readable medium produce an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.


The computer program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks.


The flowchart and block diagrams in the Figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present technology. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems that perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.


In the following description, for purposes of explanation and not limitation, specific details are set forth, such as particular embodiments, procedures, techniques, etc. in order to provide a thorough understanding of the present invention. However, it will be apparent to one skilled in the art with this disclosure before them that the present invention may be practiced in other embodiments that depart from these specific details.


Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” or “according to one embodiment” (or other phrases having similar import) at various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. Furthermore, depending on the context of discussion herein, a singular term may include its plural forms and a plural term may include its singular form. Similarly, a hyphenated term (e.g., “co-located”) may be occasionally interchangeably used with its non-hyphenated version (e.g., “co-located”), a capitalized entry (e.g., “Software”) may be interchangeably used with its non-capitalized version (e.g., “software”), a plural term may be indicated with or without an apostrophe (e.g., PE's or PEs), and an italicized term (e.g., “N+1”) may be interchangeably used with its non-italicized version (e.g., “N+1”). Such occasional interchangeable uses shall not be considered inconsistent with each other.


The terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of the invention. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms “comprises” and/or “comprising,” when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof.


It is noted at the outset that the terms “coupled,” “connected,” “connecting,” “electrically connected,” etc., are used interchangeably herein to generally refer to the condition of being electrically/electronically connected. Similarly, a first entity is considered to be in “communication” with a second entity (or entities) when the first entity electrically sends and/or receives (whether through wireline or wireless means) information signals (whether containing data information or non-data/control information) to the second entity regardless of the type (analog or digital) of those signals. It is further noted that various figures (including component diagrams) shown and discussed herein are for illustrative purpose only, and are not drawn to scale.


While specific embodiments of, and examples for, the system are described above for illustrative purposes, various equivalent modifications are possible within the scope of the system, as those skilled in the relevant art will recognize. For example, while processes or steps are presented in a given order, alternative embodiments may perform routines having steps in a different order, and some processes or steps may be deleted, moved, added, subdivided, combined, and/or modified to provide alternative or sub-combinations. Each of these processes or steps may be implemented in a variety of different ways. Also, while processes or steps are at times shown as being performed in series, these processes or steps may instead be performed in parallel, or may be performed at different times.


While various embodiments have been described above, it should be understood that they have been presented by way of example only, and not limitation. The descriptions are not intended to limit the scope of the invention to the particular forms set forth herein. To the contrary, the present descriptions are intended to cover such alternatives, modifications, and equivalents as may be included within the spirit and scope of the invention as defined by the appended claims and otherwise appreciated by one of ordinary skill in the art. Thus, the breadth and scope of a preferred embodiment should not be limited by any of the above-described exemplary embodiments.

Claims
  • 1. A method comprising: receiving at one or more servers an encrypted analytic from one or more clients, the analytic encrypted using a homomorphic encryption scheme, the analytic comprising a query;evaluating the encrypted analytic over a target data source without decrypting the encrypted analytic;grouping similar result elements of encrypted analytic evaluation;compressing the groups of similar result elements;evaluating the encrypted analytic over each of the compressed groups of result elements to generate an encrypted response, without decrypting the encrypted response and without decrypting the encrypted analytic; andsending the encrypted response from the one or more servers to the one or more clients for decompression and decryption at the one or more clients.
  • 2. The method of claim 1, further comprising co-locating two or more result elements of the target data source on a same server.
  • 3. The method of claim 1, further comprising co-locating two or more groups of result elements on a server based on a probability that the result elements are similar.
  • 4. The method of claim 1, further comprising converting the grouped result elements into byte streams.
  • 5. The method of claim 1, wherein grouping the result elements of the target data source is based on a probability that the result elements are similar.
  • 6. The method of claim 1, wherein the analytic is a model.
  • 7. The method of claim 1, wherein the homomorphic encryption scheme is Paillier encryption.
  • 8. The method of claim 1, wherein the analytic is a chain of a series of operations to be performed over the target data source.
  • 9. The method of claim 1, wherein the analytic is received from the one or more clients via the Internet.
  • 10. A system comprising: a client processor configured to execute instructions stored in memory for performing the following steps: encrypting an analytic using a homomorphic encryption scheme and an encryption key, wherein the analytic comprises a query,sending the encrypted analytic to a server without the encryption key, anddecrypting an encrypted response using the homomorphic encryption scheme and the encryption key; andthe server configured to: receive the encrypted analytic without the encryption key from the client processor via a network,evaluate the encrypted analytic over a target data source without decrypting the encrypted analytic,group similar result elements of the target data source evaluation,compress the groups of similar result elements,evaluate the encrypted analytic over the compressed groups of result elements to generate an encrypted response, without decrypting the encrypted analytic and without decrypting the encrypted response, andsend the encrypted response to the client processor.
  • 11. The system of claim 10, wherein the server is further configured to co-locate similar result elements from another server.
  • 12. The system of claim 10, wherein the client processor is further configured to decompress the response after decrypting the encrypted response.
  • 13. The system of claim 10, wherein the server is further configured convert the result elements into byte streams.
  • 14. The system of claim 10, wherein the result elements are grouped based on a probability that the result elements are similar.
  • 15. A non-transitory computer readable storage media having a program embodied thereon, the program being executable by a processor to perform a method for secure analytics of a target data source, the method comprising: receiving an encrypted analytic from a client via a network, the analytic comprising a query, the analytic encrypted using a homomorphic encryption scheme and a public encryption key, the encrypted analytic received without a corresponding private encryption key;evaluating the encrypted analytic over the target data source to generate encrypted result elements without decrypting the encrypted analytic;grouping similar result elements of the encrypted analytic evaluation without decrypting the result elements;compressing the groups of similar result elements;evaluating the encrypted analytic over the compressed groups of result elements to generate an encrypted response, without decrypting the encrypted analytic and without decrypting the encrypted response; andsending the encrypted response to the client.
  • 16. The non-transitory computer readable storage media of claim 15, the method further comprising co-locating two or more groups of result elements on a server based on a probability that the result elements are similar.
  • 17. The non-transitory computer readable storage media of claim 15, the method further comprising converting the grouped result elements into byte streams.
  • 18. The non-transitory computer readable storage media of claim 15, wherein grouping the result elements of the target data source is based on a probability that the result elements are similar.
CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit and priority of U.S. Provisional Application Ser. No. 62/448,916, filed on Jan. 20, 2017; U.S. Provisional Application Ser. No. 62/448,883, filed on Jan. 20, 2017; U.S. Provisional Application 62/448,885, filed on Jan. 20, 2017; and U.S. Provisional Application Ser. No. 62/462,818, filed on Feb. 23, 2017, all of which are hereby incorporated by reference herein, including all references and appendices, for all purposes.

US Referenced Citations (199)
Number Name Date Kind
5732390 Katayanagi et al. Mar 1998 A
6178435 Schmookler Jan 2001 B1
6745220 Hars Jun 2004 B1
6748412 Ruehle Jun 2004 B2
6910059 Lu et al. Jun 2005 B2
7712143 Comlekoglu May 2010 B2
7849185 Rockwood Dec 2010 B1
7870398 Perng et al. Jan 2011 B2
7937270 Smaragdis et al. May 2011 B2
8515058 Gentry Aug 2013 B1
8565435 Gentry et al. Oct 2013 B2
8781967 Tehranchi Jul 2014 B2
8832465 Gulati et al. Sep 2014 B2
9059855 Johnson et al. Jun 2015 B2
9094378 Yung et al. Jul 2015 B1
9189411 Mckeen et al. Nov 2015 B2
9215219 Krendelev et al. Dec 2015 B1
9288039 Monet et al. Mar 2016 B1
9491111 Roth et al. Nov 2016 B1
9503432 El Emam et al. Nov 2016 B2
9514317 Martin et al. Dec 2016 B2
9565020 Camenisch et al. Feb 2017 B1
9577829 Roth et al. Feb 2017 B1
9652609 Kang et al. May 2017 B2
9846787 Johnson et al. Dec 2017 B2
9852306 Cash et al. Dec 2017 B2
9942032 Kornaropoulos et al. Apr 2018 B1
9946810 Trepetin et al. Apr 2018 B1
9973334 Hibshoosh et al. May 2018 B2
10027486 Liu Jul 2018 B2
10055602 Deshpande et al. Aug 2018 B2
10073981 Arasu et al. Sep 2018 B2
10075288 Khedr et al. Sep 2018 B1
10120893 Rocamora et al. Nov 2018 B1
10129028 Kamakari et al. Nov 2018 B2
10148438 Evancich et al. Dec 2018 B2
10181049 El Defrawy et al. Jan 2019 B1
10210266 Antonopoulos et al. Feb 2019 B2
10235539 Ito et al. Mar 2019 B2
10255454 Kamara et al. Apr 2019 B2
10333715 Chu et al. Jun 2019 B2
10375042 Chaum Aug 2019 B2
10396984 French et al. Aug 2019 B2
10423806 Cerezo Sanchez Sep 2019 B2
10489604 Yoshino et al. Nov 2019 B2
10496631 Tschudin et al. Dec 2019 B2
10644876 Williams et al. May 2020 B2
10693627 Carr Jun 2020 B2
10721057 Carr Jul 2020 B2
10728018 Williams et al. Jul 2020 B2
10771237 Williams et al. Sep 2020 B2
10790960 Williams et al. Sep 2020 B2
10817262 Carr et al. Oct 2020 B2
10873568 Williams Dec 2020 B2
10880275 Williams Dec 2020 B2
10902133 Williams et al. Jan 2021 B2
10903976 Williams et al. Jan 2021 B2
10972251 Carr Apr 2021 B2
11196540 Williams et al. Dec 2021 B2
11196541 Williams et al. Dec 2021 B2
20020032712 Miyasaka et al. Mar 2002 A1
20020073316 Collins et al. Jun 2002 A1
20020104002 Nishizawa et al. Aug 2002 A1
20030037087 Rarick Feb 2003 A1
20030059041 MacKenzie et al. Mar 2003 A1
20030110388 Pavlin et al. Jun 2003 A1
20040167952 Gueron et al. Aug 2004 A1
20050008152 MacKenzie Jan 2005 A1
20050076024 Takatsuka et al. Apr 2005 A1
20050259817 Ramzan et al. Nov 2005 A1
20060008080 Higashi et al. Jan 2006 A1
20060008081 Higashi et al. Jan 2006 A1
20070053507 Smaragdis et al. Mar 2007 A1
20070095909 Chaum May 2007 A1
20070140479 Wang et al. Jun 2007 A1
20070143280 Wang et al. Jun 2007 A1
20090037504 Hussain Feb 2009 A1
20090083546 Staddon et al. Mar 2009 A1
20090193033 Ramzan et al. Jul 2009 A1
20090268908 Bikel et al. Oct 2009 A1
20090279694 Takahashi et al. Nov 2009 A1
20090287837 Felsher Nov 2009 A1
20100202606 Almeida Aug 2010 A1
20100205430 Chiou et al. Aug 2010 A1
20100241595 Felsher Sep 2010 A1
20110026781 Osadchy et al. Feb 2011 A1
20110107105 Hada May 2011 A1
20110110525 Gentry May 2011 A1
20110243320 Halevi et al. Oct 2011 A1
20110283099 Nath et al. Nov 2011 A1
20120039469 Mueller et al. Feb 2012 A1
20120054485 Tanaka et al. Mar 2012 A1
20120066510 Weinman Mar 2012 A1
20120201378 Nabeel et al. Aug 2012 A1
20120265794 Niel Oct 2012 A1
20120265797 Niel Oct 2012 A1
20130010950 Kerschbaum Jan 2013 A1
20130051551 El Aimani Feb 2013 A1
20130054665 Felch Feb 2013 A1
20130114811 Boufounos et al. May 2013 A1
20130148868 Troncoso Pastoriza et al. Jun 2013 A1
20130170640 Gentry Jul 2013 A1
20130191650 Balakrishnan et al. Jul 2013 A1
20130195267 Alessio et al. Aug 2013 A1
20130198526 Goto Aug 2013 A1
20130216044 Gentry et al. Aug 2013 A1
20130230168 Takenouchi Sep 2013 A1
20130237242 Oka et al. Sep 2013 A1
20130246813 Mori et al. Sep 2013 A1
20130326224 Yavuz Dec 2013 A1
20130339722 Krendelev et al. Dec 2013 A1
20130339751 Sun et al. Dec 2013 A1
20130346741 Kim et al. Dec 2013 A1
20130346755 Nguyen et al. Dec 2013 A1
20140164758 Ramamurthy et al. Jun 2014 A1
20140189811 Taylor et al. Jul 2014 A1
20140233727 Rohloff et al. Aug 2014 A1
20140281511 Kaushik et al. Sep 2014 A1
20140355756 Iwamura et al. Dec 2014 A1
20150100785 Joye et al. Apr 2015 A1
20150100794 Joye et al. Apr 2015 A1
20150205967 Naedele et al. Jul 2015 A1
20150215123 Kipnis et al. Jul 2015 A1
20150227930 Quigley et al. Aug 2015 A1
20150229480 Joye et al. Aug 2015 A1
20150244517 Nita Aug 2015 A1
20150248458 Sakamoto Sep 2015 A1
20150304736 Lal et al. Oct 2015 A1
20150358152 Ikarashi et al. Dec 2015 A1
20150358153 Gentry Dec 2015 A1
20160004874 Ioannidis et al. Jan 2016 A1
20160036826 Pogorelik et al. Feb 2016 A1
20160072623 Joye et al. Mar 2016 A1
20160105402 Kupwade-Patil et al. Apr 2016 A1
20160105414 Bringer et al. Apr 2016 A1
20160119346 Chen et al. Apr 2016 A1
20160140348 Nawaz et al. May 2016 A1
20160179945 Lastra Diaz et al. Jun 2016 A1
20160182222 Rane et al. Jun 2016 A1
20160323098 Bathen Nov 2016 A1
20160335450 Yoshino et al. Nov 2016 A1
20160344557 Chabanne et al. Nov 2016 A1
20160350648 Gilad-Bachrach et al. Dec 2016 A1
20170070340 Hibshoosh et al. Mar 2017 A1
20170070351 Yan Mar 2017 A1
20170099133 Gu et al. Apr 2017 A1
20170134158 Pasol et al. May 2017 A1
20170185776 Robinson et al. Jun 2017 A1
20170264426 Joye et al. Sep 2017 A1
20170366562 Zhang et al. Dec 2017 A1
20180091466 Friedman et al. Mar 2018 A1
20180139054 Chu et al. May 2018 A1
20180198601 Laine et al. Jul 2018 A1
20180204284 Cerezo Sanchez Jul 2018 A1
20180212751 Williams et al. Jul 2018 A1
20180212752 Williams et al. Jul 2018 A1
20180212753 Williams Jul 2018 A1
20180212754 Williams et al. Jul 2018 A1
20180212755 Williams et al. Jul 2018 A1
20180212756 Carr Jul 2018 A1
20180212758 Williams et al. Jul 2018 A1
20180212759 Williams et al. Jul 2018 A1
20180212775 Williams Jul 2018 A1
20180212933 Williams Jul 2018 A1
20180224882 Carr Aug 2018 A1
20180234254 Camenisch et al. Aug 2018 A1
20180267981 Sirdey et al. Sep 2018 A1
20180270046 Carr Sep 2018 A1
20180276417 Cerezo Sanchez Sep 2018 A1
20180343109 Koseki et al. Nov 2018 A1
20180349632 Bent et al. Dec 2018 A1
20180359097 Lindell Dec 2018 A1
20180373882 Veugen Dec 2018 A1
20190013950 Becker et al. Jan 2019 A1
20190042786 Williams et al. Feb 2019 A1
20190108350 Bohli et al. Apr 2019 A1
20190158272 Chopra et al. May 2019 A1
20190229887 Ding et al. Jul 2019 A1
20190238311 Zheng Aug 2019 A1
20190251553 Ma et al. Aug 2019 A1
20190251554 Ma et al. Aug 2019 A1
20190253235 Zhang et al. Aug 2019 A1
20190260585 Kawai et al. Aug 2019 A1
20190266282 Mitchell et al. Aug 2019 A1
20190280880 Zhang et al. Sep 2019 A1
20190312728 Poeppelmann Oct 2019 A1
20190327078 Zhang et al. Oct 2019 A1
20190334716 Kocsis et al. Oct 2019 A1
20190349191 Soriente et al. Nov 2019 A1
20190371106 Kaye Dec 2019 A1
20200134200 Williams et al. Apr 2020 A1
20200150930 Carr et al. May 2020 A1
20200204341 Williams et al. Jun 2020 A1
20200382274 Williams et al. Dec 2020 A1
20200396053 Williams et al. Dec 2020 A1
20210034765 Williams et al. Feb 2021 A1
20210105256 Williams Apr 2021 A1
20210409191 Williams et al. Dec 2021 A1
20220006629 Williams et al. Jan 2022 A1
Foreign Referenced Citations (12)
Number Date Country
2887607 Jun 2015 EP
2873186 Mar 2018 EP
5680007 Mar 2015 JP
101386294 Apr 2014 KR
WO2014105160 Jul 2014 WO
WO2015094261 Jun 2015 WO
WO2016003833 Jan 2016 WO
WO2016018502 Feb 2016 WO
WO-2018091084 May 2018 WO
WO2018136801 Jul 2018 WO
WO2018136804 Jul 2018 WO
WO2018136811 Jul 2018 WO
Non-Patent Literature Citations (39)
Entry
Petition to Insitute Derivation Proceeding Pursuant to 35 USC 135; Case No. DER2019-00009, US Patent and Trademark Office Patent Trial and Appeal Board; Jul. 26, 2019, 272 pages. (2 PDFs).
SCAMP Working Paper L29/11, “A Woods Hole Proposal Using Striping,” Dec. 2011, 14 pages.
D'Hara, Michael James, “Shovel-ready Private Information Retrieval,” Dec. 2015, 4 pages.
Darr, Benjamin et al., “Proposed Laughing Owl,” NSA Technical Report, Jan. 5, 2016, 18 pages.
Williams, Ellison Anne et al., “Wideskies: Scalable Private Informaton Retrieval,” 14 pages.
Darr, Benjamin et al., “A Private Stream Search Technique,” NSA Technical Report, Dec. 1, 2015, 18 pages.
Drucker et al., “Paillier-encrypted databases with fast aggregated queries,” 2017 14th IEEE Annual Consumer Communications & Networking Conference (CCNC), Jan. 8-11, 2017, pp. 848-853.
Tu et al., “Processing Analytical Queries over Encrypted Data,” Proceedings of the VLDB Endowment, vol. 6, Issue No. 5, Mar. 13, 2013. pp. 289-300.
Boneh et al., “Private Database Queries Using Somewhat Homomorphic Encryption”, Cryptology ePrint Archive: Report 2013/422, Standford University [online], Jun. 27, 2013, [retrieved on Dec. 9, 2019], 22 pages.
Chen et al., “Efficient Multi-Key Homomorphic Encryption with Packed Ciphertexts with Application to Oblivious Neural Network Inference”, CCS '19 Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security, May 19, 2019. pp. 395-412.
Armknecht et al., “A Guide to Fully Homomorphic Encryption” IACR Cryptology ePrint Archive: Report 2015/1192 [online], Dec. 14, 2015, 35 pages.
Bayar et al., “A Deep Learning Approach To Universal Image Manipulation Detection Using A New Convolutional Layer”, IH&MMSec 2016, Jun. 20-22, 2016. pp. 5-10.
Juvekar et al. “Gazelle: A Low Latency Framework for Secure Neural Network Inference”, 27th USENIX Security Symposium, Aug. 15-17, 2018. pp. 1650-1668.
“International Search Report” and “Written Opinion of the International Searching Authority,” Patent Cooperation Treaty Application No. PCT/US2018/014535, dated Apr. 19, 2018, 9 pages.
“International Search Report” and “Written Opinion of the International Searching Authority,” Patent Cooperation Treaty Application No. PCT/US2018/014530, dated Apr. 23, 2018, 7 pages.
“International Search Report” and “Written Opinion of the International Searching Authority,” Patent Cooperation Treaty Application No. PCT/US2018/014551, dated Apr. 24, 2018, 8 pages.
Williams, Ellison Anne et al., “Wideskies: Scalable Private Information Retrieval,” Jun. 8, 2016, 14 pages.
Panda et al., “FPGA Prototype of Low Latency BBS PRNG,” IEEE International Symposium on Nanoelectronic and Information Systems, Dec. 2015, pp. 118-123, 7 pages.
Sahu et al., “Implementation of Modular Multiplication for RSA Algorithm,” 2011 International Conference on Communication Systems and Network Technologies, 2011, pp. 112-114, 3 pages.
Drucker et al., “Achieving trustworthy Homomorphic Encryption by combining it with a Trusted Execution Environment,” Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Application (JoWUA), Mar. 2018, pp. 86-99.
Google Scholar, search results for “trusted execution environment database”, 2 pages, Aug. 1, 2020.
Bösch et al., “SOFIR: Securely Outsourced Forensic Recognition,” 2014 IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP), IEEE 978-1-4799-2893-4/14, 2014, pp. 2713-2717.
Waziri et al., “Big Data Analytics and Data Security in the Cloud via Fullly Homomorphic Encryption,” World Academy of Science, Engineering and Technology International Journal of Computer, Electrical, Automation, Control and Information Engineering, vol. 9, No. 3, 2015, pp. 744-753.
Bajpai et al., “A Fully Homomorphic Encryption Implementation on Cloud Computing,” International Journal of Information & Computation Technology, ISSN 0974-2239 vol. 4, No. 8, 2014, pp. 811-816.
Viejo et al., “Asymmetric homomorphisms for secure aggregation in heterogeneous scenarios,” Information Fusion 13, Elsevier B.V., Mar. 21, 2011, pp. 285-295.
Patil et al, “Big Data Privacy Using Fully Homomorphic Non-Deterministic Encryption,” IEEE 7th International Advance Computing Conference, Jan. 5-7, 2017, 15 pages.
Fan et al., “Somewhat Practical Fully Homomorphic Encryption”, IACR Cryptol. ePrintArch. 2012, 19 pages.
“Microsoft Computer Dictionary”, pp. 276 and 529, Microsoft Press, 5th Edition, ISBN 0735614954,2002, (Year 2002), 4 pages.
“Homomorphic encryption”, Wikipedia, May 22, 2021, pages.
PIRK Code Excerpt—QuerierDriver, https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/querier/wideskies/QuerierDriver.java; Jul. 11, 2016; 5 pages.
PIRK Code Excerpt—QuerierDriverCLI, https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/querier/wideskies/QuerierCLI.java; Jul. 11, 2016; 9 pages.
PIRK Code Excerpt—Query; [online]; Retreived from the Internet: <URL: https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/query/wideskies/Query.java>; Jul. 11, 2016; 7 pages.
PIRK Code Excerpt—QueryInfo; [online]; Retreived from the Internet: <URL: https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/query/wideskies/QueryInfo.java>; Jul. 11, 2016; 4 pages.
PIRK Code Excerpt—ComputeResponse; [online]; Retreived from the Internet: <URL: https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/responder/wideskies/spark/ComputeResponse.java>; Jul. 11, 2016; 8 pages.
PIRK Code Excerpt—HashSelectorsAndPartitionData; [online]; Retreived from the Internet: <URL: https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/responder/wideskies/spark/HashSelectorsAndPartitionData.java>; Jul. 11, 2016; 2 pages.
PIRK Code Excerpt—HashSelectorsAndFormPartitionsBigInteger; [online]; Retreived from the Internet: <URL: https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/responder/wideskies/common/HashSelectorAndPartitionData.java>; Jul. 11, 2016; 3 pages.
PIRK Code Excerpt—QueryUtils; [online]; Retreived from the Internet: <URL: https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/query/wideskies/QueryUtils.java>; Jul. 11, 2016; 8 pages.
PIRK Code Excerpt—QuerySchema; [online]; Retreived from the Internet: <URL: https://github.com/apache/incubator-retired-pirk/blob/master/src/main/java/org/apache/pirk/schema/query/QuerySchema.java>; Jul. 11, 2016; 3 pages.
“PIRK Proposal” Apache.org [online], [retreived on Oct. 28, 20]; Retreived from the Internet: <URL:https://cwiki.apache.org/confluence/display/incubator/PirkProposal>; Apr. 10, 2019; 5 pages.
Related Publications (1)
Number Date Country
20180212757 A1 Jul 2018 US
Provisional Applications (4)
Number Date Country
62462818 Feb 2017 US
62448885 Jan 2017 US
62448883 Jan 2017 US
62448916 Jan 2017 US