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Privacy Amplification by Decentralization

9 December 2020

Papers citing "Privacy Amplification by Decentralization"

33 / 33 papers shown

Title
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model Tudor Cebere A. Bellet Nicolas Papernot 82 10 0 23 May 2024
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling Vitaly Feldman Audra McMillan Kunal Talwar FedML 70 161 0 23 Dec 2020
Throughput-Optimal Topology Design for Cross-Silo Federated Learning Othmane Marfoq Chuan Xu Giovanni Neglia Richard Vidal FedML 94 88 0 23 Oct 2020
Private Weighted Random Walk Stochastic Gradient Descent Ghadir Ayache S. E. Rouayheb FedML 37 19 0 03 Sep 2020
Privacy Amplification via Random Check-Ins Borja Balle Peter Kairouz H. B. McMahan Om Thakkar Abhradeep Thakurta FedML 75 72 0 13 Jul 2020
Connecting Robust Shuffle Privacy and Pan-Privacy Victor Balcer Albert Cheu Matthew Joseph Jieming Mao FedML 68 42 0 20 Apr 2020
Inverting Gradients -- How easy is it to break privacy in federated learning? Jonas Geiping Hartmut Bauermeister Hannah Dröge Michael Moeller FedML 103 1,228 0 31 Mar 2020
A Unified Theory of Decentralized SGD with Changing Topology and Local Updates Anastasia Koloskova Nicolas Loizou Sadra Boreiri Martin Jaggi Sebastian U. Stich FedML 83 506 0 23 Mar 2020
Decentralized gradient methods: does topology matter? Giovanni Neglia Chuan Xu Don Towsley G. Calbi 59 50 0 28 Feb 2020
Pure Differentially Private Summation from Anonymous Messages Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Rasmus Pagh A. Velingker FedML 93 46 0 05 Feb 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 256 6,261 0 10 Dec 2019
Pan-Private Uniformity Testing Kareem Amin Matthew Joseph Jieming Mao 46 24 0 04 Nov 2019
Differentially Private Summation with Multi-Message Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 59 47 0 20 Jun 2019
The Privacy Blanket of the Shuffle Model Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 68 238 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 186 427 0 29 Nov 2018
Privacy Amplification by Iteration Vitaly Feldman Ilya Mironov Kunal Talwar Abhradeep Thakurta FedML 79 174 0 20 Aug 2018
Distributed Differential Privacy via Shuffling Albert Cheu Adam D. Smith Jonathan R. Ullman David Zeber M. Zhilyaev FedML 90 352 0 04 Aug 2018
Privacy Amplification by Subsampling: Tight Analyses via Couplings and Divergences Borja Balle Gilles Barthe Marco Gaboardi 82 389 0 04 Jul 2018
Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms Xueru Zhang Mohammad Mahdi Khalili M. Liu FedML 99 90 0 06 Jun 2018
Walkman: A Communication-Efficient Random-Walk Algorithm for Decentralized Optimization Xianghui Mao Kun Yuan Yubin Hu Yuantao Gu Ali H. Sayed W. Yin 60 58 0 18 Apr 2018
D $^2$ : Decentralized Training over Decentralized Data Hanlin Tang Xiangru Lian Ming Yan Ce Zhang Ji Liu 37 350 0 19 Mar 2018
Empirical Risk Minimization in Non-interactive Local Differential Privacy: Efficiency and High Dimensional Case Di Wang Marco Gaboardi Jinhui Xu 89 61 0 12 Feb 2018
Asynchronous Decentralized Parallel Stochastic Gradient Descent Xiangru Lian Wei Zhang Ce Zhang Ji Liu ODL 46 500 0 18 Oct 2017
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible Kai Zheng Wenlong Mou Liwei Wang 95 41 0 11 Jun 2017
Can Decentralized Algorithms Outperform Centralized Algorithms? A Case Study for Decentralized Parallel Stochastic Gradient Descent Xiangru Lian Ce Zhang Huan Zhang Cho-Jui Hsieh Wei Zhang Ji Liu 50 1,228 0 25 May 2017
Renyi Differential Privacy Ilya Mironov 77 1,259 0 24 Feb 2017
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 261 4,135 0 18 Oct 2016
Decentralized Collaborative Learning of Personalized Models over Networks Paul Vanhaesebrouck A. Bellet Marc Tommasi FedML 52 230 0 17 Oct 2016
Gossip Dual Averaging for Decentralized Optimization of Pairwise Functions Igor Colin A. Bellet Joseph Salmon Stephan Clémençon FedML 65 106 0 08 Jun 2016
Differentially Private Distributed Online Learning Chencheng Li Pan Zhou FedML 66 74 0 25 May 2015
Differentially Private Distributed Optimization Zhenqi Huang Sayan Mitra Nitin H. Vaidya FedML 74 265 0 12 Jan 2014
Stochastic Gradient Descent for Non-smooth Optimization: Convergence Results and Optimal Averaging Schemes Ohad Shamir Tong Zhang 148 574 0 08 Dec 2012
What Can We Learn Privately? S. Kasiviswanathan Homin K. Lee Kobbi Nissim Sofya Raskhodnikova Adam D. Smith 137 1,466 0 06 Mar 2008