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

Angelou, Nick; Benaissa, Ayoub; Cebere, Bogdan; Clark, William; Hall, Adam James; Hoeh, Michael A.; Liu, Daniel; Papadopoulos, Pavlos; Roehm, Robin; Sandmann, Robert; Schoppmann, Phillipp; Titcombe, Tom


Nick Angelou

Ayoub Benaissa

Bogdan Cebere

William Clark

Adam James Hall

Michael A. Hoeh

Daniel Liu

Robin Roehm

Robert Sandmann

Phillipp Schoppmann

Tom Titcombe


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.

Presentation Conference Type Poster
Conference Name NeurIPS 2020 Workshop on Privacy Preserving Machine Learning (PPML 2020)
Start Date Dec 11, 2020
Deposit Date Oct 31, 2022
Publicly Available Date Nov 1, 2022
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