Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth
Expansion

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Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion

10 March 2020

Weijie J. Su

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Papers citing "Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion"

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Title
Attack-Aware Noise Calibration for Differential Privacy B. Kulynych Juan Felipe Gomez G. Kaissis Flavio du Pin Calmon Carmela Troncoso 96 7 0 02 Jul 2024
Shifted Interpolation for Differential Privacy Jinho Bok Weijie Su Jason M. Altschuler 111 9 0 01 Mar 2024
Unified Enhancement of Privacy Bounds for Mixture Mechanisms via $f$ -Differential Privacy Chendi Wang Buxin Su Jiayuan Ye Reza Shokri Weijie J. Su FedML 80 11 0 30 Oct 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 76 13 0 08 Jul 2023
Practical Differentially Private and Byzantine-resilient Federated Learning Zihang Xiang Tianhao Wang Wanyu Lin Di Wang FedML 73 23 0 15 Apr 2023
Breaking the Communication-Privacy-Accuracy Tradeoff with $f$ -Differential Privacy Richeng Jin Z. Su C. Zhong Zhaoyang Zhang Tony Q.S. Quek H. Dai FedML 59 2 0 19 Feb 2023
Differentially Private Natural Language Models: Recent Advances and Future Directions Lijie Hu Ivan Habernal Lei Shen Di Wang AAML 87 19 0 22 Jan 2023
Differentially Private Bootstrap: New Privacy Analysis and Inference Strategies Zhanyu Wang Guang Cheng Jordan Awan 92 9 0 12 Oct 2022
Analytical Composition of Differential Privacy via the Edgeworth Accountant Hua Wang Sheng-yang Gao Huanyu Zhang Milan Shen Weijie J. Su FedML 71 22 0 09 Jun 2022
Rejoinder: Gaussian Differential Privacy Jinshuo Dong Aaron Roth Weijie J. Su 38 2 0 05 Apr 2021
Federated $f$ -Differential Privacy Qinqing Zheng Shuxiao Chen Qi Long Weijie J. Su FedML 149 55 0 22 Feb 2021