Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
This work provides a practical, multi-platform library for asymmetric PSI, addressing privacy concerns in contact tracing and vertical federated machine learning for developers and researchers.
This paper introduces an open-source library for asymmetric private set intersection (PSI) and PSI-Cardinality (PSI-C), which combines DDH-based protocols with Bloom filter compression to reduce communication. The library supports multiple languages and platforms, and is applied to privacy-preserving contact tracing and federated machine learning.
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.