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

• URL: http://arxiv.org/abs/2011.09350v1
• Date: Wed, 18 Nov 2020 15:38:59 GMT
• Title: Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
• Authors: Nick Angelou, Ayoub Benaissa, Bogdan Cebere, William Clark, Adam James Hall, Michael A. Hoeh, Daniel Liu, Pavlos Papadopoulos, Robin Roehm, Robert Sandmann, Phillipp Schoppmann, Tom Titcombe
• Abstract summary: We present a cross-platform 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. Our library supports C++, C, Go, WebAssembly, JavaScript, Python, and Rust, and runs on both traditional hardware (x86) and browser targets.
• Score: 7.443117783492293
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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