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Three Variants of Differential Privacy: Lossless Conversion and Applications

14 August 2020

Papers citing "Three Variants of Differential Privacy: Lossless Conversion and Applications"

24 / 24 papers shown

Title
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees Juan Felipe Gomez B. Kulynych G. Kaissis Jamie Hayes Borja Balle Antti Honkela 104 0 0 13 Mar 2025
Data value estimation on private gradients Zijian Zhou Xinyi Xu Daniela Rus Bryan Kian Hsiang Low 106 0 0 22 Dec 2024
Trustworthy Federated Learning: Privacy, Security, and Beyond Chunlu Chen Ji Liu Haowen Tan Xingjian Li Kevin I-Kai Wang Peng Li Kouichi Sakurai Dejing Dou FedML 103 11 0 03 Nov 2024
Formalization of Differential Privacy in Isabelle/HOL Tetsuya Sato Yasuhiko Minamide 26 1 0 20 Oct 2024
Shifted Interpolation for Differential Privacy Jinho Bok Weijie Su Jason M. Altschuler 115 9 0 01 Mar 2024
A Randomized Approach for Tight Privacy Accounting Jiachen T. Wang Saeed Mahloujifar Tong Wu R. Jia Prateek Mittal 106 10 0 17 Apr 2023
Practical Differentially Private and Byzantine-resilient Federated Learning Zihang Xiang Tianhao Wang Wanyu Lin Di Wang FedML 73 23 0 15 Apr 2023
$z$ -SignFedAvg: A Unified Stochastic Sign-based Compression for Federated Learning Zhiwei Tang Yanmeng Wang Tsung-Hui Chang FedML 70 14 0 06 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 92 19 0 22 Jan 2023
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
Cactus Mechanisms: Optimal Differential Privacy Mechanisms in the Large-Composition Regime Wael Alghamdi S. Asoodeh Flavio du Pin Calmon O. Kosut Lalitha Sankar Fei Wei 38 10 0 25 Jun 2022
Privacy Amplification via Shuffled Check-Ins Seng Pei Liew Satoshi Hasegawa Tsubasa Takahashi FedML 103 0 0 07 Jun 2022
A New Dimensionality Reduction Method Based on Hensel's Compression for Privacy Protection in Federated Learning Ahmed El Ouadrhiri Ahmed M Abdelhadi 41 6 0 01 May 2022
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri Laurens van der Maaten 165 55 0 28 Jan 2022
Differential Privacy in Cognitive Radio Networks: A Comprehensive Survey Muneeb Ul Hassan M. H. Rehmani Maaz Rehan Jinjun Chen 80 16 0 03 Nov 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
Differentially Private Sliced Wasserstein Distance A. Rakotomamonjy L. Ralaivola 62 24 0 05 Jul 2021
Optimal Accounting of Differential Privacy via Characteristic Function Yuqing Zhu Jinshuo Dong Yu Wang 64 104 0 16 Jun 2021
Photonic Differential Privacy with Direct Feedback Alignment Ruben Ohana H. M. Ruiz Julien Launay Alessandro Cappelli Iacopo Poli L. Ralaivola A. Rakotomamonjy 87 8 0 07 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
Local Differential Privacy Is Equivalent to Contraction of $E_γ$ -Divergence S. Asoodeh Maryam Aliakbarpour Flavio du Pin Calmon 45 30 0 02 Feb 2021
Contraction of $E_γ$ -Divergence and Its Applications to Privacy S. Asoodeh Mario Díaz Flavio du Pin Calmon 65 0 0 20 Dec 2020
Topology-aware Differential Privacy for Decentralized Image Classification Shangwei Guo Tianwei Zhang Guowen Xu Hanzhou Yu Tao Xiang Yang Liu 85 18 0 14 Jun 2020