Distributed Differential Privacy via Shuffling

4 August 2018

Papers citing "Distributed Differential Privacy via Shuffling"

50 / 191 papers shown

Title
Transfer Learning In Differential Privacy's Hybrid-Model Reʾuven Kohen Or Sheffet 21 6 0 28 Jan 2022
Differentially Private Reinforcement Learning with Linear Function Approximation Xingyu Zhou 28 25 0 18 Jan 2022
Mitigating Leakage from Data Dependent Communications in Decentralized Computing using Differential Privacy Riad Ladjel N. Anciaux A. Bellet Guillaume Scerri FedML 36 0 0 23 Dec 2021
Pure Differential Privacy from Secure Intermediaries Albert Cheu Chao Yan FedML 27 9 0 19 Dec 2021
Efficient Differentially Private Secure Aggregation for Federated Learning via Hardness of Learning with Errors Timothy Stevens Christian Skalka C. Vincent J. Ring Samuel Clark Joseph P. Near FedML 27 71 0 13 Dec 2021
Privacy Amplification via Shuffling for Linear Contextual Bandits Evrard Garcelon Kamalika Chaudhuri Vianney Perchet Matteo Pirotta FedML 35 18 0 11 Dec 2021
Applying the Shuffle Model of Differential Privacy to Vector Aggregation Mary Scott Graham Cormode Carsten Maple FedML 18 3 0 10 Dec 2021
Differentially Private Exploration in Reinforcement Learning with Linear Representation Paul Luyo Evrard Garcelon A. Lazaric Matteo Pirotta 54 11 0 02 Dec 2021
Differentially Private Federated Learning on Heterogeneous Data Maxence Noble A. Bellet Aymeric Dieuleveut FedML 13 102 0 17 Nov 2021
HydraGAN A Multi-head, Multi-objective Approach to Synthetic Data Generation Chance N. DeSmet D. Cook 28 0 0 13 Nov 2021
Frequency Estimation in the Shuffle Model with Almost a Single Message Qiyao Luo Yilei Wang K. Yi FedML 35 11 0 12 Nov 2021
Tight Bounds for Differentially Private Anonymized Histograms Pasin Manurangsi PICV 30 6 0 05 Nov 2021
Towards Sparse Federated Analytics: Location Heatmaps under Distributed Differential Privacy with Secure Aggregation Eugene Bagdasaryan Peter Kairouz S. Mellem Adria Gascon Kallista A. Bonawitz D. Estrin Marco Gruteser 24 28 0 03 Nov 2021
Differentially Private Federated Bayesian Optimization with Distributed Exploration Zhongxiang Dai K. H. Low Patrick Jaillet FedML 13 40 0 27 Oct 2021
PRECAD: Privacy-Preserving and Robust Federated Learning via Crypto-Aided Differential Privacy Xiaolan Gu Ming Li Lishuang Xiong FedML 20 10 0 22 Oct 2021
User-Level Private Learning via Correlated Sampling Badih Ghazi Ravi Kumar Pasin Manurangsi FedML 42 13 0 21 Oct 2021
Infinitely Divisible Noise in the Low Privacy Regime Rasmus Pagh N. Stausholm FedML 33 2 0 13 Oct 2021
Label differential privacy via clustering Hossein Esfandiari Vahab Mirrokni Umar Syed Sergei Vassilvitskii FedML 26 26 0 05 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 65 36 0 27 Sep 2021
Uniformity Testing in the Shuffle Model: Simpler, Better, Faster C. Canonne Hongyi Lyu FedML 26 6 0 20 Aug 2021
Selective MPC: Distributed Computation of Differentially Private Key-Value Statistics Thomas Humphries Rasoul Akhavan Mahdavi Shannon Veitch Florian Kerschbaum 29 11 0 26 Jul 2021
Differential Privacy in the Shuffle Model: A Survey of Separations Albert Cheu FedML 41 39 0 25 Jul 2021
Defending against Reconstruction Attack in Vertical Federated Learning Jiankai Sun Yuanshun Yao Weihao Gao Junyuan Xie Chong-Jun Wang AAML FedML 24 28 0 21 Jul 2021
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning Antonious M. Girgis Deepesh Data Suhas Diggavi FedML 30 21 0 19 Jul 2021
Shuffle Private Stochastic Convex Optimization Albert Cheu Matthew Joseph Jieming Mao Binghui Peng FedML 31 25 0 17 Jun 2021
On Large-Cohort Training for Federated Learning Zachary B. Charles Zachary Garrett Zhouyuan Huo Sergei Shmulyian Virginia Smith FedML 21 113 0 15 Jun 2021
A Shuffling Framework for Local Differential Privacy Casey Meehan A. Chowdhury Kamalika Chaudhuri Somesh Jha 35 0 0 11 Jun 2021
Private Counting from Anonymous Messages: Near-Optimal Accuracy with Vanishing Communication Overhead Badih Ghazi Ravi Kumar Pasin Manurangsi Rasmus Pagh FedML 32 48 0 08 Jun 2021
Differentially Private Multi-Armed Bandits in the Shuffle Model J. Tenenbaum Haim Kaplan Yishay Mansour Uri Stemmer FedML 19 28 0 05 Jun 2021
Tight Accounting in the Shuffle Model of Differential Privacy A. Koskela Mikko A. Heikkilä Antti Honkela FedML 17 17 0 01 Jun 2021
Locally Private $k$ -Means Clustering with Constant Multiplicative Approximation and Near-Optimal Additive Error Anamay Chaturvedi Matthew D. Jones Huy Le Nguyen 9 4 0 31 May 2021
On the Renyi Differential Privacy of the Shuffle Model Antonious M. Girgis Deepesh Data Suhas Diggavi A. Suresh Peter Kairouz 22 44 0 11 May 2021
Locally Private k-Means in One Round Alisa Chang Badih Ghazi Ravi Kumar Pasin Manurangsi 39 30 0 20 Apr 2021
Randomized Algorithms for Scientific Computing (RASC) A. Buluç T. Kolda Stefan M. Wild M. Anitescu Anthony Degennaro ... D. Vrabie B. Wohlberg Stephen J. Wright Chao Yang Peter Zwart AI4CE 43 10 0 19 Apr 2021
Differentially Private Histograms in the Shuffle Model from Fake Users Albert Cheu M. Zhilyaev FedML 40 27 0 06 Apr 2021
The Sample Complexity of Distribution-Free Parity Learning in the Robust Shuffle Model Kobbi Nissim Chao Yan 32 1 0 29 Mar 2021
Lossless Compression of Efficient Private Local Randomizers Vitaly Feldman Kunal Talwar 24 40 0 24 Feb 2021
Label Leakage and Protection in Two-party Split Learning Oscar Li Jiankai Sun Xin Yang Weihao Gao Hongyi Zhang Junyuan Xie Virginia Smith Chong-Jun Wang FedML 134 139 0 17 Feb 2021
Genomic Data Sharing under Dependent Local Differential Privacy Emre Yilmaz Tianxi Ji Erman Ayday Pan Li 11 21 0 15 Feb 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 44 232 0 12 Feb 2021
Deep Learning with Label Differential Privacy Badih Ghazi Noah Golowich Ravi Kumar Pasin Manurangsi Chiyuan Zhang 42 144 0 11 Feb 2021
Differentially Private Distributed Computation via Public-Private Communication Networks Lei Wang Yang Liu I. Manchester Guodong Shi FedML 29 3 0 05 Jan 2021
Hiding Among the Clones: A Simple and Nearly Optimal Analysis of Privacy Amplification by Shuffling Vitaly Feldman Audra McMillan Kunal Talwar FedML 15 157 0 23 Dec 2020
DISCO: Dynamic and Invariant Sensitive Channel Obfuscation for deep neural networks Abhishek Singh Ayush Chopra Vivek Sharma Ethan Garza Emily Zhang Praneeth Vepakomma Ramesh Raskar 25 45 0 20 Dec 2020
Research Challenges in Designing Differentially Private Text Generation Mechanisms Oluwaseyi Feyisetan Abhinav Aggarwal Zekun Xu Nathanael Teissier 14 8 0 10 Dec 2020
Privacy Amplification by Decentralization Edwige Cyffers A. Bellet FedML 44 39 0 09 Dec 2020
Improving Utility of Differentially Private Mechanisms through Cryptography-based Technologies: a Survey Wen Huang Shijie Zhou Tianqing Zhu Yongjian Liao FedML 11 1 0 02 Nov 2020
Local Differential Privacy for Regret Minimization in Reinforcement Learning Evrard Garcelon Vianney Perchet Ciara Pike-Burke Matteo Pirotta 24 32 0 15 Oct 2020
Privacy Enhancement via Dummy Points in the Shuffle Model Xiaochen Li Weiran Liu Hanwen Feng Kunzhe Huang Jinfei Liu K. Ren Zhan Qin FedML 28 5 0 29 Sep 2020
On the Round Complexity of the Shuffle Model A. Beimel Iftach Haitner Kobbi Nissim Uri Stemmer FedML 15 15 0 28 Sep 2020