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

2020/11/18 by Nick Angelou, Angelou, Nick, Ayoub Benaissa +21 · 2 citations
Computer Science · Social Sciences · #Cryptography and Data Security #Cryptography and Security (cs.CR) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Privacy, Security, and Data Protection #Privacy-Preserving Technologies in Data

paper · pdf · doi:10.48550/arxiv.2011.09350

openalex publication_date 2020/11/18 · openalex created_date 2020/11/23 · openalex updated_date 2026/07/28

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.

Citations

• The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]
• Private federated learning on vertically partitioned data via entity resolution and additively homomorphic encryption
• A generic framework for privacy preserving deep learning
• Advances and Open Problems in Federated Learning

Cited by

• Personalised Federated Learning On Heterogeneous Feature Spaces
• Practical Defences Against Model Inversion Attacks for Split Neural Networks

Related

• PPContactTracing: A Privacy-Preserving Contact Tracing Protocol for\n COVID-19 Pandemic
• DP-PSI: Private and Secure Set Intersection
• Epione: Lightweight Contact Tracing with Strong Privacy
• Function Secret Sharing for PSI-CA:With Applications to Private Contact Tracing
• IDCloak: A Practical Secure Multi-party Dataset Join Framework for Vertical Privacy-preserving Machine Learning