Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling
2021 IEEE 62nd Annual Symposium on Foundations of Computer Science (FOCS)(2021)
摘要
Recent work of Erlingsson, Feldman, Mironov, Raghunathan, Talwar, and Thakurta [1] demonstrates that random shuffling amplifies differential privacy guarantees of locally randomized data. Such amplification implies substan-tially stronger privacy guarantees for systems in which data is contributed anonymously [2] and has lead to significant interest in the shuffle model of privacy [3], [1]. We giv...
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关键词
Computer science,Privacy,Differential privacy,Stochastic processes,Machine learning,Approximation algorithms,Frequency estimation
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