Optimal Compression of Locally Differentially Private Mechanisms

29 October 2021

Papers citing "Optimal Compression of Locally Differentially Private Mechanisms"

21 / 21 papers shown

Title
Correlated Privacy Mechanisms for Differentially Private Distributed Mean Estimation Sajani Vithana V. Cadambe Flavio du Pin Calmon Haewon Jeong FedML 50 1 0 03 Jul 2024
Beyond Statistical Estimation: Differentially Private Individual Computation via Shuffling Shaowei Wang Changyu Dong Xiangfu Song Jin Li Zhili Zhou Di Wang Han Wu 41 0 0 26 Jun 2024
Universal Exact Compression of Differentially Private Mechanisms Yanxiao Liu Wei-Ning Chen Ayfer Özgür Cheuk Ting Li 42 2 0 28 May 2024
Some Notes on the Sample Complexity of Approximate Channel Simulation Gergely Flamich Lennie Wells 32 3 0 07 May 2024
Improved Communication-Privacy Trade-offs in $L_2$ Mean Estimation under Streaming Differential Privacy Wei-Ning Chen Berivan Isik Peter Kairouz Albert No Sewoong Oh Zheng Xu 57 3 0 02 May 2024
$L_q$ Lower Bounds on Distributed Estimation via Fisher Information Wei-Ning Chen Ayfer Özgür 31 1 0 02 Feb 2024
Compression with Exact Error Distribution for Federated Learning Mahmoud Hegazy Rémi Leluc Cheuk Ting Li Aymeric Dieuleveut FedML 18 9 0 31 Oct 2023
Communication Efficient Private Federated Learning Using Dithering Burak Hasircioglu Deniz Gunduz FedML 45 7 0 14 Sep 2023
Communication-Efficient Laplace Mechanism for Differential Privacy via Random Quantization Ali Moradi Shahmiri Chih Wei Ling Cheuk Ting Li 19 8 0 13 Sep 2023
Achieving the Exactly Optimal Privacy-Utility Trade-Off with Low Communication Cost via Shared Randomness Seungsoo Nam Hyun-Young Park Si-Hyeon Lee 21 2 0 08 Jul 2023
Adaptive Compression in Federated Learning via Side Information Berivan Isik Francesco Pase Deniz Gunduz Sanmi Koyejo Tsachy Weissman M. Zorzi FedML 36 9 0 22 Jun 2023
Exact Optimality of Communication-Privacy-Utility Tradeoffs in Distributed Mean Estimation Berivan Isik Wei-Ning Chen Ayfer Özgür Tsachy Weissman Albert No 61 19 0 08 Jun 2023
Compression with Bayesian Implicit Neural Representations Zongyu Guo Gergely Flamich Jiajun He Zhibo Chen José Miguel Hernández-Lobato 37 25 0 30 May 2023
Exactly Optimal and Communication-Efficient Private Estimation via Block Designs Hyun-Young Park Seungsoo Nam Si-Hyeon Lee 37 3 0 02 May 2023
Privacy Amplification via Compression: Achieving the Optimal Privacy-Accuracy-Communication Trade-off in Distributed Mean Estimation Wei-Ning Chen Danni Song Ayfer Özgür Peter Kairouz FedML 31 25 0 04 Apr 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential Privacy Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 29 2 0 19 Feb 2023
Privacy-Aware Compression for Federated Learning Through Numerical Mechanism Design Chuan Guo Kamalika Chaudhuri Pierre Stock Michael G. Rabbat FedML 33 7 0 08 Nov 2022
Contraction of Locally Differentially Private Mechanisms S. Asoodeh Huanyu Zhang 26 10 0 24 Oct 2022
Private Frequency Estimation via Projective Geometry Vitaly Feldman Jelani Nelson Huy Le Nguyen Kunal Talwar 36 21 0 01 Mar 2022
Algorithms for the Communication of Samples Lucas Theis Noureldin Yosri 53 40 0 25 Oct 2021
Amplification by Shuffling: From Local to Central Differential Privacy via Anonymity Ulfar Erlingsson Vitaly Feldman Ilya Mironov A. Raghunathan Kunal Talwar Abhradeep Thakurta 147 420 0 29 Nov 2018