论文标题
不对称的私人设置交叉点与应用程序联系跟踪和私人垂直联合机器学习
Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
论文作者
论文摘要
我们提出了一个多语言，跨平台的开源库，用于非对称私有集合（PSI）和psi-cardinality（PSI-C）。我们的协议将基于DDH的传统PSI和PSI-C协议与基于BLOOM过滤器的压缩结合在一起，有助于减少不对称设置中的通信。当前，我们的库支持C ++，C，GO，WebAssembly，JavaScript，Python和Rust，并在传统硬件（X86）和浏览器目标上运行。我们进一步将库应用于两种用例：（i）保存隐私的联系跟踪协议，与现有方法兼容，但可以改善其隐私保证，以及（ii）在垂直分区数据上具有隐私的机器学习。
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.
