Private Set Intersection via Hash-Based Indexing
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Solution Overview
Problem
Existing solutions for private set intersections require extensive comparisons between large datasets, leading to inefficiencies when trying to find common elements without revealing sensitive information.
Innovation Solution
The use of a multi-party computation function, such as the 'privatecompare' function, encodes dataset elements into tables and applies a public hash function to generate unique indices, reducing comparisons and maintaining privacy by only revealing shared elements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If each data point in the first set is compared with each data point in the second set, then the intersection can be found, but the number of comparisons becomes (m×n) which is infeasible for large datasets
Solution Approach 1:
The patent introduces hash functions as an intermediary mechanism to transform original data points into hash values. Instead of directly comparing (m×n) data points, the system compares hash values which are computationally simpler and can be processed more efficiently. The hash function acts as a mediator that preserves the equality relationship while enabling faster comparison operations.
Solution Approach 2:
The patent transforms the comparison problem by changing the parameter representation of data points. Original data points are transformed into hash values through hashing operations, changing the parameter space from raw data to hash codes. This parameter transformation allows for more efficient comparison while maintaining the ability to identify intersections.
2Measurement precision
If traditional comparison methods are used to find intersections, then all data points can be examined, but sensitive information about non-intersection data points may be revealed
Solution Approach 1:
The hash function serves as a privacy-preserving intermediary that masks the original data values. By comparing only hash values rather than the actual data points, the system can determine intersections without exposing sensitive information about non-matching data points. The hash function mediates between the need for complete intersection detection and the requirement for data privacy.
3Measurement precision
If no hash collisions are allowed in the hash table, then intersection accuracy is maintained, but the table size must be very large reducing space efficiency
Solution Approach 1:
The patent changes the approach to handling hash collisions by modifying the parameter representation strategy. Instead of avoiding collisions through large table sizes, the system uses the collision information itself as part of the comparison process. When collisions occur, the original data points are compared to verify true matches, transforming the collision from an error condition into a verification opportunity.
Data Source
AI summary
A system and method are disclosed for comparing private sets of data. The method includes encoding first elements of a first data set such that each element of the first data set is assigned a respective number in a first table, encoding second elements of a second data set such that each element of the second data set is assigned a respective number in a second table, applying a private compare function to compute an equality of each row of the first table and the second table to yield an analysis and, based on the analysis, generating a unique index of similar elements between the first data set and the second data set.