Separating Local & Shuffled Differential Privacy via Histograms

15 November 2019

Papers citing "Separating Local & Shuffled Differential Privacy via Histograms"

19 / 19 papers shown

Title
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 50 0 0 21 Feb 2025
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 31 2 0 28 May 2023
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 26 9 0 11 Apr 2023
Algorithms with More Granular Differential Privacy Guarantees Badih Ghazi Ravi Kumar Pasin Manurangsi Thomas Steinke 54 6 0 08 Sep 2022
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy Vitaly Feldman Audra McMillan Kunal Talwar FedML 23 47 0 09 Aug 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 24 6 0 05 Nov 2021
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 37 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 62 36 0 27 Sep 2021
Shuffle Private Stochastic Convex Optimization Albert Cheu Matthew Joseph Jieming Mao Binghui Peng FedML 18 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 26 48 0 08 Jun 2021
Differentially Private Histograms in the Shuffle Model from Fake Users Albert Cheu M. Zhilyaev FedML 32 27 0 06 Apr 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 25 232 0 12 Feb 2021
On Distributed Differential Privacy and Counting Distinct Elements Lijie Chen Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 18 29 0 21 Sep 2020
Connecting Robust Shuffle Privacy and Pan-Privacy Victor Balcer Albert Cheu Matthew Joseph Jieming Mao FedML 20 41 0 20 Apr 2020
Pure Differentially Private Summation from Anonymous Messages Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Rasmus Pagh A. Velingker FedML 23 46 0 05 Feb 2020
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
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 88 278 0 02 Oct 2017