Differentially Private Multi-Armed Bandits in the Shuffle Model

Differentially Private Multi-Armed Bandits in the Shuffle Model

5 June 2021

Papers citing "Differentially Private Multi-Armed Bandits in the Shuffle Model"

12 / 12 papers shown

Title
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 70 0 0 21 Feb 2025
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 51 9 0 11 Apr 2023
On Private and Robust Bandits Yulian Wu Xingyu Zhou Youming Tao Di Wang 34 6 0 06 Feb 2023
(Private) Kernelized Bandits with Distributed Biased Feedback Fengjiao Li Xingyu Zhou Bo Ji 68 5 0 28 Jan 2023
Differentially Private Linear Bandits with Partial Distributed Feedback Fengjiao Li Xingyu Zhou Bo Ji FedML 41 14 0 12 Jul 2022
Differentially Private Stochastic Linear Bandits: (Almost) for Free Osama A. Hanna Antonious M. Girgis Christina Fragouli Suhas Diggavi FedML 43 18 0 07 Jul 2022
Distributed Differential Privacy in Multi-Armed Bandits Sayak Ray Chowdhury Xingyu Zhou 58 13 0 12 Jun 2022
Privacy Amplification via Shuffling for Linear Contextual Bandits Evrard Garcelon Kamalika Chaudhuri Vianney Perchet Matteo Pirotta FedML 57 18 0 11 Dec 2021
Shuffle Private Stochastic Convex Optimization Albert Cheu Matthew Joseph Jieming Mao Binghui Peng FedML 50 25 0 17 Jun 2021
Federated Multi-Armed Bandits Chengshuai Shi Cong Shen FedML 83 92 0 28 Jan 2021
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 150 422 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 91 279 0 02 Oct 2017