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Hypothesis Testing Interpretations and Renyi Differential Privacy

24 May 2019

Gilles Barthe

Papers citing "Hypothesis Testing Interpretations and Renyi Differential Privacy"

31 / 81 papers shown

Title
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 139 23 0 06 Oct 2022
Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning Jiachen T. Wang Saeed Mahloujifar Shouda Wang R. Jia Prateek Mittal AAML 82 5 0 16 Sep 2022
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks Chulin Xie Yunhui Long Pin-Yu Chen Qinbin Li Arash Nourian Sanmi Koyejo Bo Li FedML 133 13 0 08 Sep 2022
Bayesian and Frequentist Semantics for Common Variations of Differential Privacy: Applications to the 2020 Census Daniel Kifer John M. Abowd Robert Ashmead Ryan Cumings-Menon Philip Leclerc Ashwin Machanavajjhala William Sexton Pavel I Zhuravlev 104 27 0 07 Sep 2022
The Saddle-Point Accountant for Differential Privacy Wael Alghamdi S. Asoodeh Flavio du Pin Calmon Juan Felipe Gomez O. Kosut Lalitha Sankar Fei Wei 65 7 0 20 Aug 2022
Accelerating Vertical Federated Learning Dongqi Cai Tao Fan Yan Kang Lixin Fan Mengwei Xu Shangguang Wang Qiang Yang FedML 61 8 0 23 Jul 2022
Improving Privacy-Preserving Vertical Federated Learning by Efficient Communication with ADMM Chulin Xie Pin-Yu Chen Qinbin Li Arash Nourian Ce Zhang Bo Li FedML 86 16 0 20 Jul 2022
A Generative Framework for Personalized Learning and Estimation: Theory, Algorithms, and Privacy Kaan Ozkara Antonious M. Girgis Deepesh Data Suhas Diggavi FedML 63 3 0 05 Jul 2022
Shuffle Gaussian Mechanism for Differential Privacy Seng Pei Liew Tsubasa Takahashi FedML 77 2 0 20 Jun 2022
Confidentiality Protection in the 2020 US Census of Population and Housing John M. Abowd Michael B. Hawes 62 28 0 07 Jun 2022
Privacy Amplification via Shuffled Check-Ins Seng Pei Liew Satoshi Hasegawa Tsubasa Takahashi FedML 103 0 0 07 Jun 2022
Quantum Differential Privacy: An Information Theory Perspective C. Hirche Cambyse Rouzé Daniel Stilck França 108 64 0 22 Feb 2022
Local Differential Privacy for Belief Functions Qiyu Li Chunlai Zhou Biao Qin Zhiqiang Xu 49 3 0 17 Feb 2022
Defending against Reconstruction Attacks with Rényi Differential Privacy Pierre Stock I. Shilov Ilya Mironov Alexandre Sablayrolles AAML SILM MIACV 65 40 0 15 Feb 2022
Reconstructing Training Data with Informed Adversaries Borja Balle Giovanni Cherubin Jamie Hayes MIACV AAML 95 171 0 13 Jan 2022
Improving Differentially Private SGD via Randomly Sparsified Gradients Junyi Zhu Matthew B. Blaschko 63 5 0 01 Dec 2021
Decentralized Federated Learning through Proxy Model Sharing Shivam Kalra Junfeng Wen Jesse C. Cresswell M. Volkovs Hamid R. Tizhoosh FedML 96 100 0 22 Nov 2021
Federated Deep Learning with Bayesian Privacy Hanlin Gu Lixin Fan Bowen Li Jie Li Yan Kang Yuan Yao Qiang Yang FedML 165 23 0 27 Sep 2021
A unified interpretation of the Gaussian mechanism for differential privacy through the sensitivity index Georgios Kaissis Moritz Knolle F. Jungmann Alexander Ziller Dmitrii Usynin Daniel Rueckert 59 1 0 22 Sep 2021
Renyi Differential Privacy of the Subsampled Shuffle Model in Distributed Learning Antonious M. Girgis Deepesh Data Suhas Diggavi FedML 69 22 0 19 Jul 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu Wang 64 104 0 16 Jun 2021
On the Renyi Differential Privacy of the Shuffle Model Antonious M. Girgis Deepesh Data Suhas Diggavi A. Suresh Peter Kairouz 99 44 0 11 May 2021
Federated $f$ -Differential Privacy Qinqing Zheng Shuxiao Chen Qi Long Weijie J. Su FedML 149 55 0 22 Feb 2021
The Distributed Discrete Gaussian Mechanism for Federated Learning with Secure Aggregation Peter Kairouz Ziyu Liu Thomas Steinke FedML 111 243 0 12 Feb 2021
Three Variants of Differential Privacy: Lossless Conversion and Applications S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar 59 39 0 14 Aug 2020
Revisiting Membership Inference Under Realistic Assumptions Bargav Jayaraman Lingxiao Wang Katherine Knipmeyer Quanquan Gu David Evans 77 151 0 21 May 2020
Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion Qinqing Zheng Jinshuo Dong Qi Long Weijie J. Su FedML 70 23 0 10 Mar 2020
A Better Bound Gives a Hundred Rounds: Enhanced Privacy Guarantees via $f$ -Divergences S. Asoodeh Jiachun Liao Flavio du Pin Calmon O. Kosut Lalitha Sankar FedML 82 40 0 16 Jan 2020
Robust Aggregation for Federated Learning Krishna Pillutla Sham Kakade Zaïd Harchaoui FedML 147 669 0 31 Dec 2019
Deep Learning with Gaussian Differential Privacy Zhiqi Bu Jinshuo Dong Qi Long Weijie J. Su FedML 97 209 0 26 Nov 2019
SoK: Differential Privacies Damien Desfontaines Balázs Pejó 163 126 0 04 Jun 2019