Distributed Differential Privacy via Shuffling

4 August 2018

Papers citing "Distributed Differential Privacy via Shuffling"

50 / 191 papers shown

Title
Locally Differentially Private Frequency Estimation via Joint Randomized Response Ye Zheng Shafizur Rahman Seeam Yidan Hu Rui Zhang Yanchao Zhang 21 0 0 15 May 2025
Differential Privacy for Network Assortativity Fei Ma Jinzhi Ouyang Xincheng Hu 42 0 0 06 May 2025
Towards Trustworthy Federated Learning with Untrusted Participants Youssef Allouah R. Guerraoui John Stephan FedML 55 0 0 03 May 2025
VDDP: Verifiable Distributed Differential Privacy under the Client-Server-Verifier Setup Haochen Sun Xi He 46 0 0 30 Apr 2025
Augmented Shuffle Protocols for Accurate and Robust Frequency Estimation under Differential Privacy Takao Murakami Yuichi Sei Reo Eriguchi 40 1 0 10 Apr 2025
$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 26 0 0 07 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 53 0 0 17 Mar 2025
Learning from End User Data with Shuffled Differential Privacy over Kernel Densities Tal Wagner FedML 53 0 0 21 Feb 2025
Differentially Private Empirical Cumulative Distribution Functions Antoine Barczewski Amal Mawass Jan Ramon FedML 52 0 0 10 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 61 0 0 31 Dec 2024
Review of Mathematical Optimization in Federated Learning Shusen Yang Fangyuan Zhao Zihao Zhou Liang Shi Xuebin Ren Zongben Xu FedML AI4CE 77 0 0 02 Dec 2024
Distributed Differentially Private Data Analytics via Secure Sketching Jakob Burkhardt Hannah Keller Claudio Orlandi Chris Schwiegelshohn FedML 82 0 0 30 Nov 2024
Protection against Source Inference Attacks in Federated Learning using Unary Encoding and Shuffling Andreas Athanasiou Kangsoo Jung C. Palamidessi FedML 32 1 0 10 Nov 2024
Differential Privacy on Trust Graphs Badih Ghazi Ravi Kumar Pasin Manurangsi Serena Wang 23 1 0 15 Oct 2024
Training on Fake Labels: Mitigating Label Leakage in Split Learning via Secure Dimension Transformation Yukun Jiang Peiran Wang Chengguo Lin Ziyue Huang Yong Cheng 31 0 0 11 Oct 2024
Nebula: Efficient, Private and Accurate Histogram Estimation Ali Shahin Shamsabadi Peter Snyder Ralph Giles A. Bellet Hamed Haddadi 22 0 0 15 Sep 2024
Locally Private Histograms in All Privacy Regimes Clément L. Canonne Abigail Gentle 33 1 0 09 Aug 2024
Differentially Private Block-wise Gradient Shuffle for Deep Learning Zilong Zhang FedML 37 0 0 31 Jul 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models Jungang Yang Zhe Ji Liyao Xiang 43 0 0 22 Jul 2024
Efficient Verifiable Differential Privacy with Input Authenticity in the Local and Shuffle Model Tariq Bontekoe Hassan Jameel Asghar Fatih Turkmen 18 1 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
On Computing Pairwise Statistics with Local Differential Privacy Badih Ghazi Pritish Kamath Ravi Kumar Pasin Manurangsi Adam Sealfon FedML 37 2 0 24 Jun 2024
RASE: Efficient Privacy-preserving Data Aggregation against Disclosure Attacks for IoTs Zuyan Wang Jun Tao Dikai Zou 16 0 0 31 May 2024
The Privacy Power of Correlated Noise in Decentralized Learning Youssef Allouah Anastasia Koloskova Aymane El Firdoussi Martin Jaggi R. Guerraoui 31 4 0 02 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 35 5 0 16 Apr 2024
Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting Lisa Oakley Steven Holtzen Alina Oprea 38 0 0 26 Feb 2024
Differentially Private Decentralized Learning with Random Walks Edwige Cyffers A. Bellet Jalaj Upadhyay FedML 36 2 0 12 Feb 2024
Lotto: Secure Participant Selection against Adversarial Servers in Federated Learning Zhifeng Jiang Peng Ye Shiqi He Wei Wang Ruichuan Chen Bo Li 31 2 0 05 Jan 2024
Robustness, Efficiency, or Privacy: Pick Two in Machine Learning Youssef Allouah R. Guerraoui John Stephan OOD 26 2 0 22 Dec 2023
A Generalized Shuffle Framework for Privacy Amplification: Strengthening Privacy Guarantees and Enhancing Utility E. Chen Yang Cao Yifei Ge FedML 21 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 45 4 0 30 Nov 2023
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$ -Differential Privacy Chendi Wang Buxin Su Jiayuan Ye Reza Shokri Weijie J. Su FedML 21 10 0 30 Oct 2023
Differentially Private Multi-Site Treatment Effect Estimation Tatsuki Koga Kamalika Chaudhuri David Page OOD FedML CML 33 1 0 10 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
ULDP-FL: Federated Learning with Across Silo User-Level Differential Privacy Fumiyuki Kato Li Xiong Shun Takagi Yang Cao Masatoshi Yoshikawa FedML 25 3 0 23 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 38 13 0 27 Jul 2023
Differentially Private Heavy Hitter Detection using Federated Analytics Karan N. Chadha Junye Chen John C. Duchi Vitaly Feldman H. Hashemi O. Javidbakht Audra McMillan Kunal Talwar FedML 27 7 0 21 Jul 2023
PrivSketch: A Private Sketch-based Frequency Estimation Protocol for Data Streams Ying Li Xiaodong Lee Botao Peng Themis Palpanas Jingán Xue 10 5 0 21 Jun 2023
Differentially Private Selection from Secure Distributed Computing I. Damgaard Hannah Keller Boel Nelson Claudio Orlandi Rasmus Pagh FedML 22 2 0 07 Jun 2023
Fast Optimal Locally Private Mean Estimation via Random Projections Hilal Asi Vitaly Feldman Jelani Nelson Huy Le Nguyen Kunal Talwar FedML 45 13 0 07 Jun 2023
Pure-DP Aggregation in the Shuffle Model: Error-Optimal and Communication-Efficient Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 36 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 30 1 0 22 May 2023
TPMDP: Threshold Personalized Multi-party Differential Privacy via Optimal Gaussian Mechanism Jiandong Liu Lan Zhang Chaojie Lv Ting Yu N. Freris Xiang-Yang Li 29 0 0 18 May 2023
Amplification by Shuffling without Shuffling Borja Balle James Bell Adria Gascon FedML 40 2 0 18 May 2023
Pool Inference Attacks on Local Differential Privacy: Quantifying the Privacy Guarantees of Apple's Count Mean Sketch in Practice Andrea Gadotti Frederick Sell Reethika Ramesh Jinyuan Jia 32 18 0 14 Apr 2023
Privacy Amplification via Shuffling: Unified, Simplified, and Tightened Shaowei Wang FedML 26 9 0 11 Apr 2023
When approximate design for fast homomorphic computation provides differential privacy guarantees Arnaud Grivet Sébert Martin Zuber Oana Stan Renaud Sirdey Cédric Gouy-Pailler TPM 19 1 0 06 Apr 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
Foundation Models and Fair Use Peter Henderson Xuechen Li Dan Jurafsky Tatsunori Hashimoto Mark A. Lemley Percy Liang 33 119 0 28 Mar 2023