Connecting Robust Shuffle Privacy and Pan-Privacy

20 April 2020

Papers citing "Connecting Robust Shuffle Privacy and Pan-Privacy"

10 / 10 papers shown

Title
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 30 2 0 18 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 17 9 0 11 Apr 2023
Algorithms with More Granular Differential Privacy Guarantees Badih Ghazi Ravi Kumar Pasin Manurangsi Thomas Steinke 52 6 0 08 Sep 2022
Pure Differential Privacy from Secure Intermediaries Albert Cheu Chao Yan FedML 12 9 0 19 Dec 2021
Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh Amer Sinha FedML 55 36 0 27 Sep 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh FedML 24 48 0 08 Jun 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 10 232 0 12 Feb 2021
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 42 39 0 09 Dec 2020
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 136 420 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 81 278 0 02 Oct 2017