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

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arxiv 2011.09350 v1 pith:NUCAME4F submitted 2020-11-18 cs.CR cs.LG

Nick Angelou , Ayoub Benaissa , Bogdan Cebere , William Clark , Adam James Hall , Michael A. Hoeh , Daniel Liu , Pavlos Papadopoulos
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Robin Roehm Robert Sandmann Phillipp Schoppmann Tom Titcombe
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classification cs.CRcs.LG
keywords asymmetriclibraryprivatecontactintersectionlearningmachineprivacy-preserving
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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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  1. Multi-client Functional Encryption for Set Intersection with Non-monotonic Access Structures in Federated Learning

    cs.CR 2024-12 reject novelty 5.0 of 10

    A functional encryption scheme for set intersection with non-monotonic access control is constructed and analyzed, but the write-up has critical specification and proof gaps.

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