Private Collection Matching Protocols

TL;DR

This paper introduces a modular framework for privacy-preserving collection matching, enabling efficient and privacy-aware determination of set matches in real-world applications like chemical and document datasets.

Contribution

The paper presents a novel modular framework for private collection matching that improves efficiency and privacy compared to existing cryptographic methods.

Findings

Communication cost scales linearly with client's set size

Framework achieves privacy gain over existing methods

Demonstrated effectiveness on chemical and document datasets

Abstract

We introduce Private Collection Matching (PCM) problems, in which a client aims to determine whether a collection of sets owned by a server matches their interests. Existing privacy-preserving cryptographic primitives cannot solve PCM problems efficiently without harming privacy. We propose a modular framework that enables designers to build privacy-preserving PCM systems that output one bit: whether a collection of server sets matches the client's set. The communication cost of our protocols scales linearly with the size of the client's set and is independent of the number of server elements. We demonstrate the potential of our framework by designing and implementing novel solutions for two real-world PCM problems: determining whether a dataset has chemical compounds of interest, and determining whether a document collection has relevant documents. Our evaluation shows that we offer a privacy gain with respect to existing works at a reasonable communication and computation cost.