Homomorphic Data Exchange for Privacy-Preserving Cross-Repository Queries
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Solution Overview
Problem
Current data sharing across disparate repositories is hindered by privacy concerns, preventing the aggregation and analysis of data from multiple sources without compromising sensitive information.
Innovation Solution
Implementing homomorphic encryption to store and analyze data in a central repository without decryption, allowing secure aggregation and analysis of data from multiple sources while maintaining privacy, using a central repository that receives and stores homomorphically encrypted data, processes requests, and delivers encrypted results with one-time decryption keys.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If data is shared across multiple repositories to increase sample size and genetic diversity, then the value and power of data analysis is improved, but data privacy and security are compromised due to exposure of unencrypted data
Solution Approach 1:
Homomorphic encryption serves as an intermediary mechanism that enables data to be processed in encrypted form. The encryption system acts as a mediator between data privacy requirements and data analysis needs, allowing computations to be performed on encrypted data without exposing the underlying plaintext information. This resolves the contradiction by enabling cross-repository data sharing while maintaining security through the intermediary encryption layer.
Solution Approach 2:
The patent changes the state of data from unencrypted to homomorphically encrypted, transforming the data's cryptographic parameters. This parameter change allows the data to simultaneously maintain security properties and enable analytical operations. By modifying the encryption state rather than the data content itself, the system achieves both privacy preservation and data utility across multiple repositories.
2Reliability
If data is encrypted to maintain privacy, then data security is improved, but the ability to query and analyze data across repositories is lost
Solution Approach 1:
The patent replaces the traditional mechanical approach of decrypting data for analysis with a cryptographic substitution approach. Instead of converting encrypted data back to plaintext for querying, the system performs computations directly on the encrypted data using homomorphic operations. This substitution maintains security while enabling query capabilities that would otherwise require decryption.
Solution Approach 2:
The system changes the operational parameters of data processing by transitioning from plaintext operations to homomorphic encrypted operations. This parameter change in the computational domain allows queries to be executed on encrypted data, simultaneously maintaining security and preserving ease of operation for data analysis across repositories.
3Quantity of substance
If data from multiple independent sources is aggregated, then sample size and genetic diversity are increased, but the complexity of maintaining data privacy across sources increases
Solution Approach 1:
Homomorphic encryption provides a universal solution that works across multiple independent data sources with different privacy requirements. The same encryption and computation framework can be applied to aggregate data from any number of repositories, whether genomic data, medical records, or other sensitive information. This multi-functional approach simplifies privacy management across diverse sources by providing a single, unified mechanism for secure aggregation.
Data Source
AI summary
The present disclosure generally relates to the secure exchange of data. In some implementations, an example method involves receiving a data request from a data requester. The method also includes identifying from the data request, one or more types of data for which presence may be determined by a number of independent data sources, providing the identified one or more types of data to the number of independent data sources for determining presence of the identified one or more types of data, and receiving from at least one of the number of independent data sources data corresponding to the identified one or more types of data. The method also includes aggregating the data received from each of the independent data sources, and providing the aggregated data to the data requester.


