Distributed privacy-preserving computing on protected data

Abstract

The present disclosure relates to techniques for developing artificial intelligence algorithms by distributing analytics to multiple sources of privacy protected, harmonized data. Particularly, aspects are directed to a computer implemented method that includes receiving an algorithm and input data requirements associated with the algorithm, identifying data assets as being available from a data host based on the input data requirements, curating the data assets within a data storage structure that is within infrastructure of the data host, and integrating the algorithm into a secure capsule computing framework. The secure capsule computing framework serves the algorithm to the data assets within the data storage structure in a secure manner that preserves privacy of the data assets and the algorithm. The computer implemented method further includes running the data assets through the algorithm to obtain an inference.

Description

[0001] Cross References to Related Applications [0002] This application claims U.S. Provisional Application No. 62 / 948,556, entitled "DISTRIBUTED PRIVACY-PRESERVING COMPUTING ON PROTECTED DATA," filed December 16, 2019 and filed March 2019 Priority and Benefit of U.S. Provisional Application No. 62 / 824,183, entitled "FEDERATED MACHINE LEARNING TECHNIQUES FOR HIGHLY CURATED HEALTH-CARE DATA SETS" filed on the 26th , the entire contents of both provisional applications are hereby incorporated by reference for all purposes. technical field [0003] The present invention relates to privacy-preserving computing, and in particular to techniques (e.g., systems, methods) for developing artificial intelligence applications and / or algorithms by using distributed analytics on multiple sources of privacy-preserving harmonized data , a computer program product storing code or instructions executable by one or more processors). The invention is particularly effective for developing arti...

AI Technical Summary

Problems solved by technology

However, most AI algorithm developers lack healthcare data assets of sufficient fidelity and diversity to train, test, and validate their algorithms and models without overcoming significant barriers to accessing the data

Additionally, even when AI algorithm developers have sufficient data assets, few developers perform third-party validation, resulting in algorithms and models that are essentially proof-of-concept studies rather than solutions that can be applied to production or clinical settings

Further development of AI models and algorithms for production or clinical use appears to be severely hampered by one major obstacle: timely access to high-fidelity, diverse, privacy-preserving data

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