Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness

2 June 2019

Papers citing "Heterogeneous Gaussian Mechanism: Preserving Differential Privacy in Deep Learning with Provable Robustness"

9 / 9 papers shown

Title
XRand: Differentially Private Defense against Explanation-Guided Attacks Truc D. T. Nguyen Phung Lai Nhathai Phan My T. Thai AAML SILM 30 14 0 08 Dec 2022
Lifelong DP: Consistently Bounded Differential Privacy in Lifelong Machine Learning Phung Lai Han Hu Nhathai Phan Ruoming Jin My T. Thai An M. Chen 25 2 0 26 Jul 2022
Encoded Gradients Aggregation against Gradient Leakage in Federated Learning Dun Zeng Shiyu Liu Siqi Liang Zonghang Li Hongya Wang Irwin King Zenglin Xu FedML 34 0 0 26 May 2022
One Parameter Defense -- Defending against Data Inference Attacks via Differential Privacy Dayong Ye Sheng Shen Tianqing Zhu B. Liu Wanlei Zhou MIACV 16 62 0 13 Mar 2022
Task-aware Privacy Preservation for Multi-dimensional Data Jiangnan Cheng A. Tang Sandeep P. Chinchali 33 7 0 05 Oct 2021
Partial sensitivity analysis in differential privacy Tamara T. Mueller Alexander Ziller Dmitrii Usynin Moritz Knolle F. Jungmann Daniel Rueckert Georgios Kaissis 50 1 0 22 Sep 2021
More Than Privacy: Applying Differential Privacy in Key Areas of Artificial Intelligence Tianqing Zhu Dayong Ye Wei Wang Wanlei Zhou Philip S. Yu SyDa 38 125 0 05 Aug 2020
Differentially Private and Fair Classification via Calibrated Functional Mechanism Jiahao Ding Xinyue Zhang Xiaohuan Li Junyi Wang Rong Yu Miao Pan FaML 21 39 0 14 Jan 2020
Adversarial examples in the physical world Alexey Kurakin Ian Goodfellow Samy Bengio SILM AAML 329 5,849 0 08 Jul 2016