A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
AuthorsVitaly Feldman, Audra McMillan, Kunal Talwar
Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta demonstrates that random shuffling amplifies differential privacy guarantees of locally randomized data. Such amplification implies substantially stronger privacy guarantees for systems in which data is contributed anonymously and has lead to significant interest in the shuffle model of privacy
We show that random shuffling of
Earlier this year, Apple hosted the Workshop on Privacy-Preserving Machine Learning (PPML). This virtual event brought Apple and members of the academic research communities together to discuss the state of the art in the field of privacy-preserving machine learning through a series of talks and discussions over two days.