Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message

27 September 2021

Papers citing "Differentially Private Aggregation in the Shuffle Model: Almost Central Accuracy in Almost a Single Message"

29 / 29 papers shown

Title
$Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High $\varepsilon$ Regime$ Infinitely Divisible Noise for Differential Privacy: Nearly Optimal Error in the High $\varepsilon$ Regime Charlie Harrison Pasin Manurangsi 24 0 0 07 Apr 2025
PREAMBLE: Private and Efficient Aggregation of Block Sparse Vectors and Applications Hilal Asi Vitaly Feldman Hannah Keller G. Rothblum Kunal Talwar FedML 54 1 0 14 Mar 2025
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 48 0 0 21 Feb 2025
Segmented Private Data Aggregation in the Multi-message Shuffle Model Shaowei Wang Hongqiao Chen Sufen Zeng Ruilin Yang Hui Jiang ... Kaiqi Yu Rundong Mei Shaozheng Huang Wei Yang Bangzhou Xin FedML 54 0 0 31 Dec 2024
Differential Privacy on Trust Graphs Badih Ghazi Ravi Kumar Pasin Manurangsi Serena Wang 18 1 0 15 Oct 2024
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling Shaowei Wang Changyu Dong Xiangfu Song Jin Li Zhili Zhou Di Wang Han Wu 33 0 0 26 Jun 2024
Making Old Things New: A Unified Algorithm for Differentially Private Clustering Max Dupré la Tour Monika Henzinger David Saulpic FedML 33 1 0 17 Jun 2024
Private Vector Mean Estimation in the Shuffle Model: Optimal Rates Require Many Messages Hilal Asi Vitaly Feldman Jelani Nelson Huy Le Nguyen Kunal Talwar Samson Zhou FedML 24 5 0 16 Apr 2024
Optimal Unbiased Randomizers for Regression with Label Differential Privacy Ashwinkumar Badanidiyuru Badih Ghazi Pritish Kamath Ravi Kumar Ethan Leeman Pasin Manurangsi A. Varadarajan Chiyuan Zhang 17 4 0 09 Dec 2023
Differentially Private Aggregation via Imperfect Shuffling Badih Ghazi Ravi Kumar Pasin Manurangsi Jelani Nelson Samson Zhou FedML 20 1 0 28 Aug 2023
Samplable Anonymous Aggregation for Private Federated Data Analysis Kunal Talwar Shan Wang Audra McMillan Vojta Jina Vitaly Feldman ... Congzheng Song Karl Tarbe Sebastian Vogt L. Winstrom Shundong Zhou FedML 28 13 0 27 Jul 2023
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 18 2 0 28 May 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 22 2 0 18 May 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 13 9 0 11 Apr 2023
Multi-Message Shuffled Privacy in Federated Learning Antonious M. Girgis Suhas Diggavi FedML 18 8 0 22 Feb 2023
Regression with Label Differential Privacy Badih Ghazi Pritish Kamath Ravi Kumar Ethan Leeman Pasin Manurangsi A. Varadarajan Chiyuan Zhang 16 18 0 12 Dec 2022
Private Non-Convex Federated Learning Without a Trusted Server Andrew Lowy Ali Ghafelebashi Meisam Razaviyayn FedML 25 24 0 13 Mar 2022
Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation Kunal Talwar FedML 21 10 0 22 Feb 2022
Aggregation and Transformation of Vector-Valued Messages in the Shuffle Model of Differential Privacy Mary Scott Graham Cormode Carsten Maple 32 11 0 31 Jan 2022
Transfer Learning In Differential Privacy's Hybrid-Model Reʾuven Kohen Or Sheffet 16 6 0 28 Jan 2022
Frequency Estimation in the Shuffle Model with Almost a Single Message Qiyao Luo Yilei Wang K. Yi FedML 30 11 0 12 Nov 2021
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 21 13 0 21 Oct 2021
Infinitely Divisible Noise in the Low Privacy Regime Rasmus Pagh N. Stausholm FedML 25 2 0 13 Oct 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
Instance-optimal Mean Estimation Under Differential Privacy Ziyue Huang Yuting Liang K. Yi 13 57 0 01 Jun 2021
Locally Private k-Means in One Round Alisa Chang Badih Ghazi Ravi Kumar Pasin Manurangsi 29 30 0 20 Apr 2021
Private Aggregation from Fewer Anonymous Messages Badih Ghazi Pasin Manurangsi Rasmus Pagh A. Velingker FedML 37 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 134 420 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 79 278 0 02 Oct 2017