Connecting Robust Shuffle Privacy and Pan-Privacy

Connecting Robust Shuffle Privacy and Pan-Privacy

20 April 2020

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

14 / 14 papers shown

Title
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 48 0 0 21 Feb 2025
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 32 2 0 18 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 20 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
Tight Bounds for Differentially Private Anonymized Histograms Pasin Manurangsi PICV 17 6 0 05 Nov 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 57 36 0 27 Sep 2021
Shuffle Private Stochastic Convex Optimization Albert Cheu Matthew Joseph Jieming Mao Binghui Peng FedML 13 25 0 17 Jun 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 16 232 0 12 Feb 2021
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 42 39 0 09 Dec 2020
On Distributed Differential Privacy and Counting Distinct Elements Lijie Chen Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 13 29 0 21 Sep 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 83 278 0 02 Oct 2017