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

arXiv: https://arxiv.org/abs/2011.09350 · HTML: https://mlsanity.com/paper/2011.09350/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Nov 2020.

## Built on by (most cited first, 12 of 12)

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- [Privacy and Trust Redefined in Federated Machine Learning](https://mlsanity.com/paper/2103.15753/) (2021)
- [Multi-VFL: A Vertical Federated Learning System for Multiple Data and Label Owners](https://mlsanity.com/paper/2106.05468/) (2021)
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- [Split-U-Net: Preventing Data Leakage in Split Learning for Collaborative Multi-Modal Brain Tumor Segmentation](https://mlsanity.com/paper/2208.10553/) (2022)
- [Syft 0.5: A Platform for Universally Deployable Structured Transparency](https://mlsanity.com/paper/2104.12385/) (2021)
- [TabVFL: Improving Latent Representation in Vertical Federated Learning](https://mlsanity.com/paper/2404.17990/) (2024)
- [Secure Embedding Aggregation for Federated Representation Learning](https://mlsanity.com/paper/2206.09097/) (2022)
- [Secure Visual Data Processing via Federated Learning](https://mlsanity.com/paper/2502.06889/) (2025)

## 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.