Secure Multi-Party Computation for Privacy-Preserving Data Matching
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Solution Overview
Problem
Conventional approaches for multi-party computations require a trusted third party to facilitate data collaboration, which raises concerns about data secrecy and compliance with data retention policies, and lacks assurance that sensitive data will not be abused or accessed unauthorized.
Innovation Solution
Implementing secure multi-party computation (MPC) techniques that allow parties to jointly perform analyses on their datasets without revealing sensitive information, using shared universal identifiers to map and match vectors between datasets, enabling secure lift analysis and reach frequency measurements without a trusted intermediary.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If entities provide sensitive data to a trusted third party for collaboration, then data analysis can be performed, but data secrecy and compliance with data retention policies are compromised
Solution Approach 1:
The patent segments the trusted third party's role into multiple independent components: each entity's data remains in their own secure environment, and only encrypted computations are transmitted. This eliminates the need for a single centralized trusted party that holds all sensitive data, thereby maintaining data secrecy while enabling collaboration.
Solution Approach 2:
The patent introduces cryptographic protocols as an intermediary mechanism that enables computation between parties without requiring them to trust each other or a third party. The secure multi-party computation protocol acts as a mediator that allows data analysis while preserving data secrecy through mathematical guarantees rather than trust.
2Adaptability or versatility
If entities share sensitive data for collaboration, then insights can be gained, but unauthorized access and data abuse risks increase
Solution Approach 1:
The patent changes the fundamental parameter of data sharing from 'raw data exchange' to 'encrypted computation'. By transforming the data into mathematical representations that can be computed upon without revealing the underlying sensitive information, the system enables collaboration while eliminating unauthorized access risks.
Solution Approach 2:
The patent replaces the mechanical system of data sharing (physically or electronically transferring sensitive data between parties) with a cryptographic system that performs computations on encrypted data. This substitution eliminates the security vulnerabilities inherent in data transmission and storage.
3Productivity
If a trusted third party is used for data collaboration, then data joining and analysis can be performed, but assurance of data retention policy compliance is lost
Solution Approach 1:
The patent enables each entity to maintain control over their own data throughout the collaboration process. Each party's data remains in their own secure environment, and they can independently enforce their data retention policies without relying on a third party's trustworthiness. The computation protocol automatically ensures that data never leaves the controlling entity's secure environment.
4Measurement precision
If sensitive data is revealed to enable collaboration, then analysis effectiveness improves, but data privacy and security are compromised
Solution Approach 1:
The patent creates a composite computational framework that combines encrypted data representations with computational operations. This composite structure allows analysis to be performed on the encrypted representations, achieving analysis effectiveness without exposing the underlying sensitive data, thereby eliminating privacy risks.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can determine a set of mappings between vectors in a first dataset associated with a first party to a set of shared universal identifiers based on a secure multi-party computation. A set of mappings can be determined between vectors in a second dataset associated with a second party to the set of shared universal identifiers based on the secure multi-party computation. Membership information for each vector in the first dataset can be obtained. The membership information indicating whether an individual associated with the vector is assigned to a test group, a control group, or neither. Conversion information for each vector in the second dataset can be obtained. The conversion information indicating whether an individual converted. Conversion counts for the test group and the control group can be determined based at least in part on the membership information and the conversion information.