Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical 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 se...

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Bibliographic Details
Published inarXiv.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
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 18.11.2020
Subjects
Asymmetry
Contact tracing
Libraries
Machine learning
Privacy
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Summary: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.
ISSN:2331-8422