Multi-Party Private Set Intersection Using Share Polynomials
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Solution Overview
Problem
Existing technologies fail to securely compute the intersection of datasets held by multiple parties while preserving the privacy of individual datasets and ensuring the result is revealed only to a neutral third party, which is crucial in scenarios like cybersecurity threat detection and marketing analytics.
Innovation Solution
A cryptographic protocol that allows multiple parties to compute the intersection of their datasets through share polynomials, using pseudorandom functions and oblivious PRFs to ensure privacy, with two protocols optimizing for communication and computational efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional PSI protocols are used, then privacy of individual datasets is preserved, but the system cannot securely compute intersection for multiple parties with neutral third-party result revelation
Solution Approach 1:
The patent segments the dataset into shares using secret sharing schemes, where each party holds partial information (shares) of their dataset. This segmentation allows the third party to compute intersections of shares rather than full datasets, achieving multi-party capability while preserving individual privacy. The share polynomials encode shared data elements that can be processed without revealing original datasets.
Solution Approach 2:
The patent introduces share polynomials as an intermediary representation between the original datasets and the computation result. These polynomials encode shared data elements in a form that enables intersection computation while maintaining privacy. The share polynomials serve as the mediator that allows third-party processing without direct access to individual party datasets.
2Reliability
If share polynomials are used to encode datasets, then privacy is maintained, but computational complexity increases
Solution Approach 1:
The patent transforms the data representation from raw dataset elements to share polynomial coefficients. This parameter change allows the third party to work with polynomial evaluations rather than processing full datasets, reducing computational complexity while maintaining privacy. The share polynomials are constructed to enable efficient intersection computation through polynomial arithmetic operations.
3Loss of information
If intersection computation is performed by third party, then result revelation is controlled, but communication requirements increase
Solution Approach 1:
The patent extracts only the necessary information for intersection computation from the full datasets by using share polynomials that encode only the shared elements. This extraction allows the third party to compute intersections without receiving or processing complete datasets, reducing communication requirements while maintaining precise control over result disclosure to the third party.
Data Source
AI summary
A method computes, by a third party, a private set intersection of datasets of multiple input parties, wherein each dataset includes one or more data elements. The computer-processor-implemented method includes: obtaining one or more share polynomials for each dataset of the multiple input parties, the one or more share polynomials for a dataset of an input party being encoded from shares of zero for the input party, each share of zero corresponding to a data element of the dataset of the input party; determining an intersection polynomial based on the one or more share polynomials; and determining the private set intersection of the datasets to include data elements of the datasets of the multiple input parties for which the intersection polynomial of the multiple input parties solves to zero.