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

• Published in: arXiv.org
• Main Authors: Angelou, Nick, Ayoub Benaissa, Cebere, Bogdan, Clark, William, Hall, Adam James, Hoeh, Michael A, Liu, Daniel, Papadopoulos, Pavlos, Roehm, Robin, Sandmann, Robert, Schoppmann, Phillipp, Titcombe, Tom
• Format: Paper
• Language: English
• Published: Ithaca Cornell University Library, arXiv.org 18.11.2020
• Subjects: Asymmetry; Contact tracing; Libraries; Machine learning; Privacy

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.