Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
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Abstract (arXiv)
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.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 18 Nov 2020.
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Citing papers per year: 2021: 5; 2022: 4; 2023: 1; 2024: 1; 2025: 1.
12 papers.
- 2022A Survey on Heterogeneous Federated Learningcited by 42
- 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNNcited by 35
- 2021Practical Defences Against Model Inversion Attacks for Split Neural Networkscited by 29
- 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directionscited by 14
- 2021Privacy and Trust Redefined in Federated Machine Learningcited by 13
- 2021Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Ownerscited by 5
- 2023Personalised Federated Learning On Heterogeneous Feature Spacescited by 5
- 2022Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentationcited by 4
- 2021Syft 0.5: A Platform for Universally Deployable Structured Transparencycited by 3
- 2024TabVFL: Improving Latent Representation in Vertical Federated Learningcited by 1
- 2022Secure Embedding Aggregation for Federated Representation Learningcited by 0
- 2025Secure Visual Data Processing via Federated Learningcited by 0
- 2025Secure Visual Data Processing via Federated Learning9 Feb 2025
- 2024TabVFL: Improving Latent Representation in Vertical Federated Learning27 Apr 2024
- 2023Personalised Federated Learning On Heterogeneous Feature Spaces26 Jan 2023
- 2022A Survey on Heterogeneous Federated Learning10 Oct 2022
- 2022Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation22 Aug 2022
- 2022Combined Federated and Split Learning in Edge Computing for Ubiquitous Intelligence in Internet of Things: State of the Art and Future Directions20 Jul 2022
- 2022Secure Embedding Aggregation for Federated Representation Learning18 Jun 2022
- 2021Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners10 Jun 2021
- 2021Syft 0.5: A Platform for Universally Deployable Structured Transparency26 Apr 2021
- 2021Practical Defences Against Model Inversion Attacks for Split Neural Networks12 Apr 2021
- 2021PyVertical: A Vertical Federated Learning Framework for Multi-headed SplitNN1 Apr 2021
- 2021Privacy and Trust Redefined in Federated Machine Learning29 Mar 2021