Differentially Private Histograms in the Shuffle Model from Fake Users

6 April 2021

Papers citing "Differentially Private Histograms in the Shuffle Model from Fake Users"

9 / 9 papers shown

Title
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 48 0 0 21 Feb 2025
On the Robustness of LDP Protocols for Numerical Attributes under Data Poisoning Attacks Xiaoguang Li Zitao Li Ninghui Li Wenhai Sun AAML 87 3 0 28 Jan 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 23 9 0 11 Apr 2023
Robustness of Locally Differentially Private Graph Analysis Against Poisoning Jacob Imola A. Chowdhury Kamalika Chaudhuri AAML 20 6 0 25 Oct 2022
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 35 13 0 21 Oct 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
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
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 85 278 0 02 Oct 2017