Patent Application Titled “Hardware-Accelerated Homomorphic Encryption In Marketplace Platforms” Published Online (USPTO 20240160771): Patent Application

2024 JUN 03 (NewsRx) -- By a News Reporter-Staff News Editor at Insurance Daily News -- According to news reporting originating from Washington, D.C., by NewsRx journalists, a patent application by the inventors Hansen, Adam Wesley (Missouri City, TX, US); Hansen, Christopher Glenn (Provo, UT, US), filed on March 27, 2022, was made available online on May 16, 2024.

No assignee for this patent application has been made.

Reporters obtained the following quote from the background information supplied by the inventors: “Unless otherwise indicated herein, the materials described herein are not prior art to the claims in the present application and are not admitted to be prior art by inclusion in this section.

“All types of data in numerous different fields is being generated throughout the world. Similarly, significant amounts of data are being aggregated and stored in various repositories throughout the world, including those which are commercially or governmentally managed or held. Within a given field, the accumulated data may be used in aggregate by individual repositories for various purposes. For example, in the case of genomic data, private and public repositories are utilized for discovery of disease-gene associations and potential drug targets, identification of candidates for enrollment in clinical trials, and reclassification of variants of uncertain significance (VUS) as pathogenic or benign, amongst other possibilities. The repositories may include genomic sequencing data for millions of individuals worldwide.

“In parallel with the development of these different, and often isolated, data resources, there is often a demand for increased sample size by potential users of the data. Individual repositories can increase their sample size, but eventually their growth will plateau or level off as these repositories saturate in size due to market reach or political boundaries. In addition, data in a single repository may presently be individually queried, but the data in numerous repositories may not be queried together. In the latter instance, the ability to query or analyze data across disparate repositories would allow for greater power and value relative to a corresponding data request of any single repository due to increased sample size and genetic diversity. However, data sharing across repositories is not currently employed due to a number of drawbacks, including for example the common need of maintaining data privacy, whether due to legal obligations (e.g., to protect individual-level data) or business concerns. For example, searching across numerous independent data sources is not possible without compromising privacy by exposing unencrypted data to external parties.

“The subject matter claimed herein is not limited to implementations that solve any disadvantages or that operate only in environments such as those described above. Rather, this background is only provided to illustrate one example technology area where some implementations described herein may be practiced.”

In addition to obtaining background information on this patent application, NewsRx editors also obtained the inventors’ summary information for this patent application: “This Summary is provided to introduce a selection of concepts in a simplified form that are further described below in the Detailed Description. This Summary is not intended to identify key features or essential characteristics of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

“In an example embodiment, a method includes receiving, at a central processing unit (CPU), a data request from a data requester to search or filter data in a repository. At least a first portion of the data is homomorphically encrypted. The method includes analyzing the stored data without decrypting the homomorphically encrypted data to determine an aggregated subset of data relevant to the data request. The aggregated subset of data includes at least some of the homomorphically encrypted data. The analyzing includes dispatching, from the CPU, a command to a hardware accelerator to execute an operation on the homomorphically encrypted data. The analyzing includes executing, at the hardware accelerator, the operation on the homomorphically encrypted data. The analyzing includes receiving, at the CPU, an output of the execution of the operation by the hardware accelerator. The aggregated subset of data is based on the output. The method includes providing data request results that include or are derived from the aggregated subset of data to the data requester.

“In another example embodiment, a system includes a CPU, a hardware accelerator, and one or more non-transitory computer-readable media containing instructions which, in response to being executed by the CPU, cause the system to perform or control performance of operations. The operations include receiving, at the CPU, a data request from a data requester to search or filter data in a repository. At least a first portion of the data is homomorphically encrypted. The operations include analyzing the stored data without decrypting the homomorphically encrypted data to determine an aggregated subset of data relevant to the data request. The aggregated subset of data includes at least some of the homomorphically encrypted data. The analyzing includes dispatching, from the CPU, a command to the hardware accelerator to execute an operation on the homomorphically encrypted data. The analyzing includes executing, at the hardware accelerator, the operation on the homomorphically encrypted data. The analyzing includes receiving, at the CPU, an output of the execution of the operation. The aggregated subset of data is based on the output. The operations include providing data request results that include or are derived from the aggregated subset of data to the data requester.

“In another example embodiment, one or more non-transitory computer-readable media contain instructions which, in response to being executed by a CPU, cause a system that includes the CPU and a hardware accelerator to perform or control performance of operations. The operations include receiving, at the CPU, a data request from a data requester to search or filter data in a repository. At least a first portion of the data is homomorphically encrypted. The operations include analyzing the stored data without decrypting the homomorphically encrypted data to determine an aggregated subset of data relevant to the data request. The aggregated subset of data includes at least some of the homomorphically encrypted data. The analyzing includes dispatching, from the CPU, a command to the hardware accelerator to execute an operation on the homomorphically encrypted data. The analyzing includes executing, at the hardware accelerator, the operation on the homomorphically encrypted data. The analyzing includes receiving, at the CPU, an output of the execution of the operation. The aggregated subset of data is based on the output. The operations include providing data request results that include or are derived from the aggregated subset of data to the data requester.

“In another example embodiment, a method includes receiving, at a CPU, a request from a requester to process data in a repository. At least a first portion of the data is homomorphically encrypted. The method includes processing the data without decrypting the homomorphically encrypted data to calculate a result of a computational operation. The processing includes dispatching, from the CPU, a command to a hardware accelerator to execute an operation on the homomorphically encrypted data to complete the processing. The processing includes executing, at the hardware accelerator, the operation on the homomorphically encrypted data. The processing includes receiving, at the CPU, an output of the execution of the operation. The method includes returning the result of the computational operation, wherein the result of the computational operation includes or is based on the output of the operation executed by the hardware accelerator.

“Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by the practice of the invention. The features and advantages of the invention may be realized and obtained by means of the instruments and combinations particularly pointed out in the appended claims. These and other features of the present invention will become more fully apparent from the following description and appended claims, or may be learned by the practice of the invention as set forth hereinafter.

“all arranged in accordance with at least one embodiment described herein.”

The claims supplied by the inventors are:

“1. A method, comprising: receiving, at a central processing unit (CPU), a data request from a data requester to search or filter data in a repository, wherein at least a first portion of the data is homomorphically encrypted; analyzing the stored data without decrypting the homomorphically encrypted data to determine an aggregated subset of data relevant to the data request, the aggregated subset of data comprising at least some of the homomorphically encrypted data, the analyzing including: dispatching, from the CPU, a command to a hardware accelerator to execute an operation on the homomorphically encrypted data; executing, at the hardware accelerator, the operation on the homomorphically encrypted data; and receiving, at the CPU, an output of the execution of the operation by the hardware accelerator, wherein the aggregated subset of data is based on the output; and providing data request results that include or are derived from the aggregated subset of data to the data requester.

“2. The method of claim 1, wherein the hardware accelerator comprises a graphics processing unit (GPU).

“3. The method of claim 1, wherein the hardware accelerator comprises a field-programmable gate array (FPGA).

“4. The method of claim 1, wherein the hardware accelerator comprises an application-specific integrated circuit (ASIC).

“5. The method of claim 1, wherein the aggregated subset of data includes homomorphically encrypted data received from at least two of the independent data sources.

“6. The method of claim 1, further comprising receiving a decryption request from the data requester for decryption of the at least some of the homomorphically encrypted data included in the aggregated subset of data.

“7. The method of claim 6, further comprising providing a decryption key from at least one of the independent data sources to the data requester.

“8. The method of claim 7, wherein the aggregated subset of data includes at least some data from the at least one independent data source that has been homomorphically encrypted.

“9. The method of claim 7, wherein the decryption key is a one-time decryption key.

“10. The method of claim 6, further comprising: identifying the independent data sources having homomorphically encrypted data in the aggregated subset of data; notifying the identified independent data sources of the data request; and receiving re-encrypted data from the identified independent data sources, the re-encrypted data being re-encrypted with a public encryption key provided by the data requester.

“11. The method of claim 1, further comprising: running one or more cache queries to identify one or more locations of certain homomorphically encrypted data stored in the repository; storing the one or more locations; and identifying the one or more locations when the data request from the data requester is the same or similar to the one or more cache queries.

“12. The method of claim 1, wherein: at least a second portion of the data received from the number of independent data sources is encrypted; and the first portion of the data received from the number of independent data sources has a different sensitivity level than the second portion of the data received from the number of independent data sources.

“13. The method of claim 1, further comprising identifying from the data request received from the data requester one or more types of data to be identified from the stored data and analyzing the stored data to determine if the one or more types of data is included therein.

“14. The method of claim 13, wherein the one or more types of data to be identified from the stored data includes at least one type of genomic data, at least one type of phenotypic data, or a combination of at least one type of genomic data and at least one type of phenotypic data, and analyzing the stored data includes determining if the homomorphically encrypted data includes any instances of the at least one type of genomic data, the at least one type of phenotypic data, or a combination of at least one type of genomic data and at least one type of phenotypic data.

“15. The method of claim 14, wherein the at least one type of genomic data includes a genetic variant.

“16. The method of claim 14, wherein the at least one type of phenotypic data includes one or more of demographic information, electronic health record data and derivatives thereof, medical diagnostic codes, billing codes, terms from computational ontologies, patient-reported data, automatically generated data from health wearables or sensors, family history data, and medical imaging raw data or downstream derivative features thereof.

“17. The method of claim 1, wherein the stored data includes information relating to physical assets for sale.

“18. The method of claim 1, wherein the stored data includes at least one type of phenotypic data, the phenotypic data including one or more of demographic information, electronic health record data and derivatives thereof, medical diagnostic codes, billing codes, terms from computational ontologies, patient-reported data, automatically generated data from health wearables or sensors, family history data, and medical imaging raw data or downstream derivative features thereof.

“19. The method of claim 1, wherein the stored data includes financial information, the financial information including health insurance information, billing information, account balance information, credit information, credit score information, payment information, or any combination of the foregoing.

“20. The method of claim 1, wherein the first portion of the received data is homomorphically encrypted before receipt from the number of independent data sources.

“21. The method of claim 1, wherein the stored data includes at least one type of genomic data, at least one type of phenotypic data, or a combination of at least one type of genomic data and at least one type of phenotypic data.

“22. A system comprising: a central processing unit (CPU); a hardware accelerator; and one or more non-transitory computer-readable media containing instructions which, in response to being executed by the CPU, cause the system to perform or control performance of operations comprising: receiving, at the CPU, a data request from a data requester to search or filter data in a repository, wherein at least a first portion of the data is homomorphically encrypted; analyzing the stored data without decrypting the homomorphically encrypted data to determine an aggregated subset of data relevant to the data request, the aggregated subset of data comprising at least some of the homomorphically encrypted data, the analyzing including: dispatching, from the CPU, a command to the hardware accelerator to execute an operation on the homomorphically encrypted data; executing, at the hardware accelerator, the operation on the homomorphically encrypted data; and receiving, at the CPU, an output of the execution of the operation by the hardware accelerator, wherein the aggregated subset of data is based on the output; and providing data request results that include or are derived from the aggregated subset of data to the data requester.

“23. The system of claim 22, wherein the hardware accelerator comprises a graphics processing unit (GPU).

“24. The system of claim 22, wherein the hardware accelerator comprises a field-programmable gate array (FPGA).

“25. The system of claim 22, wherein the hardware accelerator comprises an application-specific integrated circuit (ASIC).

“26. One or more non-transitory computer-readable media containing instructions which, in response to being executed by a central processing unit (CPU), cause a system that includes the CPU and a hardware accelerator to perform or control performance of operations comprising: receiving, at the CPU, a data request from a data requester to search or filter data in a repository, wherein at least a first portion of the data is homomorphically encrypted; analyzing the stored data without decrypting the homomorphically encrypted data to determine an aggregated subset of data relevant to the data request, the aggregated subset of data comprising at least some of the homomorphically encrypted data, the analyzing including: dispatching, from the CPU, a command to the hardware accelerator to execute an operation on the homomorphically encrypted data; executing, at the hardware accelerator, the operation on the homomorphically encrypted data; and receiving, at the CPU, an output of the execution of the operation by the hardware accelerator, wherein the aggregated subset of data is based on the output; and providing data request results that include or are derived from the aggregated subset of data to the data requester.

“27. A method, comprising: receiving, at a central processing unit (CPU), a request from a requester to process data in a repository, wherein at least a first portion of the data is homomorphically encrypted; processing the data without decrypting the homomorphically encrypted data to calculate a result of a computational operation, the processing including: dispatching, from the CPU, a command to a hardware accelerator to execute an operation on the homomorphically encrypted data to complete the processing; executing, at the hardware accelerator, the operation on the homomorphically encrypted data; and receiving, at the CPU, an output of the execution of the operation; and returning the result of the computational operation, wherein the result of the computational operation includes or is based on the output of the operation executed by the hardware accelerator.

“28. The method of claim 27, further comprising aggregating the returned result with data returned or derived from another repository.”

There are additional claims. Please visit full patent to read further.

For more information, see this patent application: Hansen, Adam Wesley; Hansen, Christopher Glenn. Hardware-Accelerated Homomorphic Encryption In Marketplace Platforms. U.S. Patent Application Number 20240160771, filed March 27, 2022 and posted May 16, 2024. Patent URL (for desktop use only): https://ppubs.uspto.gov/pubwebapp/external.html?q=(20240160771)&db=US-PGPUB&type=ids

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