Differentially Private Aggregation via Imperfect Shuffling

28 August 2023

Papers citing "Differentially Private Aggregation via Imperfect Shuffling"

23 / 23 papers shown

Title
Necessary Conditions in Multi-Server Differential Privacy Albert Cheu Chao Yan 31 8 0 17 Aug 2022
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy Vitaly Feldman Audra McMillan Kunal Talwar FedML 72 49 0 09 Aug 2022
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh Amer Sinha FedML 89 36 0 27 Sep 2021
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling Vitaly Feldman Audra McMillan Kunal Talwar FedML 61 161 0 23 Dec 2020
Privacy Amplification via Random Check-Ins Borja Balle Peter Kairouz H. B. McMahan Om Thakkar Abhradeep Thakurta FedML 69 72 0 13 Jul 2020
CoinPress: Practical Private Mean and Covariance Estimation Sourav Biswas Yihe Dong Gautam Kamath Jonathan R. Ullman 55 116 0 11 Jun 2020
Advances and Open Problems in Federated Learning Peter Kairouz H. B. McMahan Brendan Avent A. Bellet M. Bennis ... Zheng Xu Qiang Yang Felix X. Yu Han Yu Sen Zhao FedML AI4CE 232 6,247 0 10 Dec 2019
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 81 57 0 24 Sep 2019
Differentially Private SQL with Bounded User Contribution Royce J. Wilson Celia Yuxin Zhang William K. C. Lam Damien Desfontaines Daniel Simmons-Marengo Bryant Gipson 64 149 0 04 Sep 2019
Locally Private k-Means Clustering Uri Stemmer FedML 71 57 0 04 Jul 2019
Differentially Private Summation with Multi-Message Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 52 47 0 20 Jun 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 61 238 0 07 Mar 2019
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 174 427 0 29 Nov 2018
Distributed Differential Privacy via Shuffling Albert Cheu Adam D. Smith Jonathan R. Ullman David Zeber M. Zhilyaev FedML 85 352 0 04 Aug 2018
cpSGD: Communication-efficient and differentially-private distributed SGD Naman Agarwal A. Suresh Felix X. Yu Sanjiv Kumar H. B. McMahan FedML 118 490 0 27 May 2018
Collecting Telemetry Data Privately Bolin Ding Janardhan Kulkarni Sergey Yekhanin 48 686 0 05 Dec 2017
Prochlo: Strong Privacy for Analytics in the Crowd Andrea Bittau Ulfar Erlingsson Petros Maniatis Ilya Mironov A. Raghunathan David Lie Mitch Rudominer Ushasree Kode J. Tinnés B. Seefeld 122 278 0 02 Oct 2017
Distributed Mean Estimation with Limited Communication A. Suresh Felix X. Yu Sanjiv Kumar H. B. McMahan FedML 97 364 0 02 Nov 2016
Federated Learning: Strategies for Improving Communication Efficiency Jakub Konecný H. B. McMahan Felix X. Yu Peter Richtárik A. Suresh Dave Bacon FedML 293 4,643 0 18 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 198 6,113 0 01 Jul 2016
RAPPOR: Randomized Aggregatable Privacy-Preserving Ordinal Response Ulfar Erlingsson Vasyl Pihur Aleksandra Korolova 92 1,990 0 25 Jul 2014
Distributed Private Data Analysis: On Simultaneously Solving How and What A. Beimel Kobbi Nissim Eran Omri FedML 106 207 0 14 Mar 2011
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 126 1,465 0 06 Mar 2008