Shuffle Private Stochastic Convex Optimization

Shuffle Private Stochastic Convex Optimization

17 June 2021

Papers citing "Shuffle Private Stochastic Convex Optimization"

19 / 19 papers shown

Title
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications Hilal Asi Vitaly Feldman Hannah Keller G. Rothblum Kunal Talwar FedML 56 1 0 14 Mar 2025
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 50 0 0 21 Feb 2025
Private and Federated Stochastic Convex Optimization: Efficient Strategies for Centralized Systems Roie Reshef Kfir Y. Levy FedML 25 0 0 17 Jul 2024
A Huber Loss Minimization Approach to Mean Estimation under User-level Differential Privacy Puning Zhao Lifeng Lai Li Shen Qingming Li Jiafei Wu Zhe Liu 47 7 0 22 May 2024
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages Hilal Asi Vitaly Feldman Jelani Nelson Huy Le Nguyen Kunal Talwar Samson Zhou FedML 27 5 0 16 Apr 2024
On Differentially Private Federated Linear Contextual Bandits Xingyu Zhou Sayak Ray Chowdhury FedML 40 15 0 27 Feb 2023
Multi-Message Shuffled Privacy in Federated Learning Antonious M. Girgis Suhas Diggavi FedML 20 8 0 22 Feb 2023
Concurrent Shuffle Differential Privacy Under Continual Observation J. Tenenbaum Haim Kaplan Yishay Mansour Uri Stemmer FedML 33 2 0 29 Jan 2023
(Private) Kernelized Bandits with Distributed Biased Feedback Fengjiao Li Xingyu Zhou Bo Ji 30 5 0 28 Jan 2023
Private Stochastic Optimization With Large Worst-Case Lipschitz Parameter: Optimal Rates for (Non-Smooth) Convex Losses and Extension to Non-Convex Losses Andrew Lowy Meisam Razaviyayn 30 13 0 15 Sep 2022
Differentially Private Linear Bandits with Partial Distributed Feedback Fengjiao Li Xingyu Zhou Bo Ji FedML 26 13 0 12 Jul 2022
Differentially Private Stochastic Linear Bandits: (Almost) for Free Osama A. Hanna Antonious M. Girgis Christina Fragouli Suhas Diggavi FedML 27 18 0 07 Jul 2022
Distributed Differential Privacy in Multi-Armed Bandits Sayak Ray Chowdhury Xingyu Zhou 25 12 0 12 Jun 2022
Private Non-Convex Federated Learning Without a Trusted Server Andrew Lowy Ali Ghafelebashi Meisam Razaviyayn FedML 28 24 0 13 Mar 2022
Shuffle Private Linear Contextual Bandits Sayak Ray Chowdhury Xingyu Zhou FedML 16 25 0 11 Feb 2022
Differential Privacy in the Shuffle Model: A Survey of Separations Albert Cheu FedML 28 39 0 25 Jul 2021
Practical and Private (Deep) Learning without Sampling or Shuffling Peter Kairouz Brendan McMahan Shuang Song Om Thakkar Abhradeep Thakurta Zheng Xu FedML 182 154 0 26 Feb 2021
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 141 420 0 29 Nov 2018
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 88 278 0 02 Oct 2017