Homomorphic Encryption for Aggregate Data Analysis
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Solution Overview
Problem
Epidemiological studies face challenges in protecting sensitive personal data, such as genetic information, from re-identification during aggregate analysis, as existing methods fail to ensure complete security and anonymity for study participants and researchers.
Innovation Solution
A computer-implemented method involving double encryption with a broker encryption key, decryption to generate single-encrypted data, aggregation using homomorphic encryption, and inclusion of random factors encoded with a garbled circuit protocol to protect participant data, ensuring that only prescribed statistical analyses can be performed without revealing individual identities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing encryption methods are used for aggregate data analysis, then data security is partially improved, but complete protection against re-identification is not achieved
Solution Approach 1:
The patent implements nested encryption by applying multiple encryption layers to the same data. First, homomorphic encryption is applied to enable computations on encrypted data. Then, garbled circuit encoding is applied to the homomorphically encrypted data. This nested structure ensures that even if one encryption layer is compromised, the data remains protected by the other layers, achieving complete protection against re-identification while maintaining data security.
Solution Approach 2:
The patent combines two different encryption methodologies (homomorphic encryption and garbled circuit encoding) into a composite encryption system. This composite approach leverages the strengths of both methods: homomorphic encryption allows statistical computations without decryption, while garbled circuit encoding provides additional obfuscation. The combination creates a more robust security system that fully prevents re-identification while maintaining the ability to perform aggregate analysis.
2Reliability
If multiple encryption layers are applied to protect data, then data security is improved, but computational complexity increases
Solution Approach 1:
The patent applies homomorphic encryption to the data before any aggregation or analysis operations are performed. This preliminary encryption step enables all subsequent statistical computations (such as calculating odds ratios and regression coefficients) to be performed directly on the encrypted data without requiring decryption. The garbled circuit encoding is also applied in advance to the homomorphically encrypted aggregate data. By performing these encryption operations preliminarily, the system avoids the need for complex real-time decryption and re-encryption operations during analysis, thus managing computational complexity while maintaining strong security.
3Reliability
If encrypted data is aggregated for statistical analysis, then participant anonymity is improved, but extraction of statistical insights becomes more difficult
Solution Approach 1:
The patent uses homomorphically encrypted data as an intermediary representation that preserves statistical properties while protecting individual identities. The homomorphic encryption scheme is specifically designed to allow statistical operations (such as computing means, variances, odds ratios, and regression coefficients) to be performed on the encrypted data. The garbled circuit encoding further obfuscates the data while maintaining the ability to extract aggregate statistics. This intermediary encrypted representation enables complete participant anonymity while preserving the necessary statistical information for epidemiological analysis.
Data Source
AI summary
Embodiments of the invention include systems and methods for protecting study participant data for aggregate analysis. Aspects include sending a broker encryption key to a plurality of subjects. Aspects also include receiving double-encrypted subject data from the plurality of subjects. Aspects also include decrypting the double-encrypted subject data with a broker decryption key to generate single-encrypted subject data for the plurality of subjects. Aspects also include aggregating the single-encrypted subject data for the plurality of subjects to generate an aggregated single-homomorphically encrypted data set. Aspects also include including a plurality of random factors in the aggregated single-encrypted data set. Aspects also include sending the aggregated single-homomorphically encrypted data set to a researcher.