Differential Privacy in the Shuffle Model: A Survey of Separations

25 July 2021

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

10 / 10 papers shown

Title
Mildly Accurate Computationally Differentially Private Inner Product Protocols Imply Oblivious Transfer Iftach Haitner N. Mazor Jad Silbak Eliad Tsfadia Chao Yan 53 1 0 24 Feb 2025
On the Complexity of Differentially Private Best-Arm Identification with Fixed Confidence Achraf Azize Marc Jourdan Aymen Al Marjani D. Basu 32 3 0 05 Sep 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 32 2 0 18 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 27 18 0 14 Apr 2023
On Privacy and Personalization in Cross-Silo Federated Learning Ziyu Liu Shengyuan Hu Zhiwei Steven Wu Virginia Smith FedML 22 51 0 16 Jun 2022
Pure Differential Privacy from Secure Intermediaries Albert Cheu Chao Yan FedML 12 9 0 19 Dec 2021
Uniformity Testing in the Shuffle Model: Simpler, Better, Faster C. Canonne Hongyi Lyu FedML 16 6 0 20 Aug 2021
Improved Summation from Shuffling Borja Balle James Bell Adria Gascon Kobbi Nissim FedML 48 22 0 24 Sep 2019
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 42 55 0 24 Sep 2019
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