The Privacy Blanket of the Shuffle Model

7 March 2019

Papers citing "The Privacy Blanket of the Shuffle Model"

50 / 142 papers shown

Title
Locally Differentially Private Frequency Estimation via Joint Randomized Response Ye Zheng Shafizur Rahman Seeam Yidan Hu Rui Zhang Yanchao Zhang 16 0 0 15 May 2025
Differential Privacy for Network Assortativity Fei Ma Jinzhi Ouyang Xincheng Hu 37 0 0 06 May 2025
Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy Takao Murakami Yuichi Sei Reo Eriguchi 27 1 0 10 Apr 2025
PAUSE: Low-Latency and Privacy-Aware Active User Selection for Federated Learning Ori Peleg Natalie Lang Stefano Rini Nir Shlezinger Kobi Cohen FedML 48 0 0 17 Mar 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
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
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 23 1 0 15 Oct 2024
Nebula: Efficient, Private and Accurate Histogram Estimation Ali Shahin Shamsabadi Peter Snyder Ralph Giles A. Bellet Hamed Haddadi 17 0 0 15 Sep 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 27 0 0 31 Jul 2024
Enhanced Privacy Bound for Shuffle Model with Personalized Privacy Yi-xiao Liu Yuhan Liu Li Xiong Yujie Gu Hong Chen FedML 37 0 0 25 Jul 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models Jungang Yang Zhe Ji Liyao Xiang 35 0 0 22 Jul 2024
Efficient Verifiable Differential Privacy with Input Authenticity in the Local and Shuffle Model Tariq Bontekoe H. Asghar Fatih Turkmen 13 0 0 27 Jun 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
Making Old Things New: A Unified Algorithm for Differentially Private Clustering Max Dupré la Tour Monika Henzinger David Saulpic FedML 41 1 0 17 Jun 2024
RASE: Efficient Privacy-preserving Data Aggregation against Disclosure Attacks for IoTs Zuyan Wang Jun Tao Dikai Zou 11 0 0 31 May 2024
FastLloyd: Federated, Accurate, Secure, and Tunable $k$ -Means Clustering with Differential Privacy Abdulrahman Diaa Thomas Humphries Florian Kerschbaum FedML 26 0 0 03 May 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 27 5 0 16 Apr 2024
Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget Jiankai Jin C. Chuengsatiansup Toby C. Murray Benjamin I. P. Rubinstein Y. Yarom Olga Ohrimenko 30 6 0 31 Jan 2024
A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility E. Chen Yang Cao Yifei Ge FedML 11 8 0 22 Dec 2023
Privacy Amplification by Iteration for ADMM with (Strongly) Convex Objective Functions T.-H. Hubert Chan Hao Xie Mengshi Zhao 32 1 0 14 Dec 2023
Toward the Tradeoffs between Privacy, Fairness and Utility in Federated Learning Kangkang Sun Xiaojin Zhang Xi Lin Gaolei Li Jing Wang Jianhua Li 33 4 0 30 Nov 2023
Differentially Private Multi-Site Treatment Effect Estimation Tatsuki Koga Kamalika Chaudhuri David Page OOD FedML CML 25 1 0 10 Oct 2023
Practical, Private Assurance of the Value of Collaboration H. Asghar Zhigang Lu Zhongrui Zhao Dali Kaafar FedML 21 0 0 04 Oct 2023
Differentially Private Aggregation via Imperfect Shuffling Badih Ghazi Ravi Kumar Pasin Manurangsi Jelani Nelson Samson Zhou FedML 30 1 0 28 Aug 2023
Shuffled Differentially Private Federated Learning for Time Series Data Analytics Chenxi Huang Chaoyang Jiang Zhenghua Chen FedML AI4TS 13 0 0 30 Jul 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 30 13 0 27 Jul 2023
Privacy Preserving Bayesian Federated Learning in Heterogeneous Settings Disha Makhija Joydeep Ghosh Nhat Ho FedML 24 2 0 13 Jun 2023
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 28 2 0 28 May 2023
Analyzing the Shuffle Model through the Lens of Quantitative Information Flow Mireya Jurado Ramon G. Gonze Mário S. Alvim C. Palamidessi 19 1 0 22 May 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 32 2 0 18 May 2023
Echo of Neighbors: Privacy Amplification for Personalized Private Federated Learning with Shuffle Model Yi-xiao Liu Suyun Zhao Li Xiong Yuhan Liu Hong Chen FedML 11 9 0 11 Apr 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 23 9 0 11 Apr 2023
Private Read-Update-Write with Controllable Information Leakage for Storage-Efficient Federated Learning with Top $r$ Sparsification Sajani Vithana S. Ulukus FedML 18 5 0 07 Mar 2023
On Differentially Private Federated Linear Contextual Bandits Xingyu Zhou Sayak Ray Chowdhury FedML 34 15 0 27 Feb 2023
Concurrent Shuffle Differential Privacy Under Continual Observation J. Tenenbaum Haim Kaplan Yishay Mansour Uri Stemmer FedML 28 2 0 29 Jan 2023
Private Federated Statistics in an Interactive Setting Audra McMillan O. Javidbakht Kunal Talwar Elliot Briggs Mike Chatzidakis ... Paul J. Pelzl Rehan Rishi Congzheng Song Shan Wang Shundong Zhou FedML 13 6 0 18 Nov 2022
Discrete Distribution Estimation under User-level Local Differential Privacy Jayadev Acharya Yuhan Liu Ziteng Sun 16 16 0 07 Nov 2022
Composition of Differential Privacy & Privacy Amplification by Subsampling Thomas Steinke 56 49 0 02 Oct 2022
Private Read Update Write (PRUW) in Federated Submodel Learning (FSL): Communication Efficient Schemes With and Without Sparsification Sajani Vithana S. Ulukus FedML 13 19 0 09 Sep 2022
Verifiable Differential Privacy Ari Biswas Graham Cormode 22 11 0 18 Aug 2022
FedPerm: Private and Robust Federated Learning by Parameter Permutation Hamid Mozaffari Virendra J. Marathe D. Dice FedML 17 4 0 16 Aug 2022
Stronger Privacy Amplification by Shuffling for Rényi and Approximate Differential Privacy Vitaly Feldman Audra McMillan Kunal Talwar FedML 18 47 0 09 Aug 2022
How Much Privacy Does Federated Learning with Secure Aggregation Guarantee? A. Elkordy Jiang Zhang Yahya H. Ezzeldin Konstantinos Psounis A. Avestimehr FedML 35 38 0 03 Aug 2022
MPC for Tech Giants (GMPC): Enabling Gulliver and the Lilliputians to Cooperate Amicably Bar Alon M. Naor Eran Omri Uri Stemmer 13 5 0 11 Jul 2022
Differentially Private Stochastic Linear Bandits: (Almost) for Free Osama A. Hanna Antonious M. Girgis Christina Fragouli Suhas Diggavi FedML 21 18 0 07 Jul 2022
"You Can't Fix What You Can't Measure": Privately Measuring Demographic Performance Disparities in Federated Learning Marc Juárez Aleksandra Korolova FedML 32 9 0 24 Jun 2022
Shuffle Gaussian Mechanism for Differential Privacy Seng Pei Liew Tsubasa Takahashi FedML 10 2 0 20 Jun 2022
Walking to Hide: Privacy Amplification via Random Message Exchanges in Network Hao Wu O. Ohrimenko Anthony Wirth FedML 14 1 0 20 Jun 2022