Selected article for: "machine learning and open source"

Author: Angelou, Nick; Benaissa, Ayoub; Cebere, Bogdan; Clark, William; Hall, Adam James; Hoeh, Michael A.; Liu, Daniel; Papadopoulos, Pavlos; Roehm, Robin; Sandmann, Robert; Schoppmann, Phillipp; Titcombe, Tom
Title: Asymmetric Private Set Intersection with Applications to Contact Tracing and Private Vertical Federated Machine Learning
  • Cord-id: lchaqbn3
  • Document date: 2020_11_18
  • ID: lchaqbn3
    Snippet: 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 c
    Document: 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.

    Search related documents:
    Co phrase search for related documents