Differential Privacy in the Shuffle Model: A Survey of Separations

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Differential Privacy in the Shuffle Model: A Survey of Separations

25 July 2021

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Papers citing "Differential Privacy in the Shuffle Model: A Survey of Separations"

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Title
Computationally Differentially Private Inner Product Protocols Imply Oblivious Transfer Iftach Haitner N. Mazor Jad Silbak Eliad Tsfadia Chao Yan 86 1 0 21 Feb 2025
Analyzing the Shuffle Model through the Lens of Quantitative Information Flow Mireya Jurado Ramon G. Gonze Mário S. Alvim C. Palamidessi 66 1 0 22 May 2023
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice Andrea Gadotti Frederick Sell Reethika Ramesh Jinyuan Jia 55 18 0 14 Apr 2023
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 113 56 0 16 Jun 2022
Shuffle Private Linear Contextual Bandits Sayak Ray Chowdhury Xingyu Zhou FedML 98 27 0 11 Feb 2022
Uniformity Testing in the Shuffle Model: Simpler, Better, Faster C. Canonne Hongyi Lyu FedML 90 6 0 20 Aug 2021
Tight Accounting in the Shuffle Model of Differential Privacy A. Koskela Mikko A. Heikkilä Antti Honkela FedML 65 17 0 01 Jun 2021
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model Kobbi Nissim Chao Yan 138 1 0 29 Mar 2021