Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning

TL;DR

This paper introduces an open-source, multi-language library for asymmetric private set intersection that reduces communication costs and is applicable to privacy-preserving contact tracing and machine learning.

Contribution

It presents a versatile, cross-platform library combining PSI protocols with Bloom filter compression, supporting multiple languages and hardware, and demonstrates its use in contact tracing and federated learning.

Findings

Supports multiple programming languages and platforms.

Reduces communication in asymmetric PSI with Bloom filters.

Enhances privacy in contact tracing and federated learning applications.

Abstract

We present a multi-language, cross-platform, open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C). Our protocol combines traditional DDH-based PSI and PSI-C protocols with compression based on Bloom filters that helps reduce communication in the asymmetric setting. Currently, our library supports C++, C, Go, WebAssembly, JavaScript, Python, and Rust, and runs on both traditional hardware (x86) and browser targets. We further apply our library to two use cases: (i) a privacy-preserving contact tracing protocol that is compatible with existing approaches, but improves their privacy guarantees, and (ii) privacy-preserving machine learning on vertically partitioned data.