Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
arXiv:2011.09350
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.
arXiv commentNeurIPS 2020 Workshop on Privacy Preserving Machine Learning (PPML 2020)
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Cited by in corpus (5)
- PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN
- Practical Defences Against Model Inversion Attacks for Split Neural Networks
- Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners
- Personalised Federated Learning On Heterogeneous Feature Spaces
- Syft 0.5: A Platform for Universally Deployable Structured Transparency