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Network Shuffling: Privacy Amplification via Random Walks. (arXiv:2204.03919v1 [cs.CR])
April 11, 2022, 1:20 a.m. | Seng Pei Liew, Tsubasa Takahashi, Shun Takagi, Fumiyuki Kato, Yang Cao, Masatoshi Yoshikawa
cs.CR updates on arXiv.org arxiv.org
Recently, it is shown that shuffling can amplify the central differential
privacy guarantees of data randomized with local differential privacy. Within
this setup, a centralized, trusted shuffler is responsible for shuffling by
keeping the identities of data anonymous, which subsequently leads to stronger
privacy guarantees for systems. However, introducing a centralized entity to
the originally local privacy model loses some appeals of not having any
centralized entity as in local differential privacy. Moreover, implementing a
shuffler in a reliable way …
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