Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity

@inproceedings{Erlingsson2019AmplificationBS,
  title={Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity},
  author={{\'U}lfar Erlingsson and V. Feldman and Ilya Mironov and A. Raghunathan and Kunal Talwar and Abhradeep Thakurta},
  booktitle={SODA},
  year={2019}
}
  • Úlfar Erlingsson, V. Feldman, +3 authors Abhradeep Thakurta
  • Published in SODA 2019
  • Computer Science, Mathematics
  • Sensitive statistics are often collected across sets of users, with repeated collection of reports done over time. [...] Key Result As a practical corollary, our results imply that several LDP-based industrial deployments may have much lower privacy cost than their advertised e would indicate---at least if reports are anonymized.Expand Abstract
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