Against Membership Inference Attack: Pruning is All You Need

Against Membership Inference Attack: Pruning is All You Need

28 August 2020

Caiwen Ding

Sanguthevar Rajasekaran

Papers citing "Against Membership Inference Attack: Pruning is All You Need"

7 / 7 papers shown

Title
Trustworthy Distributed AI Systems: Robustness, Privacy, and Governance Wenqi Wei Ling Liu 31 16 0 02 Feb 2024
AutoReP: Automatic ReLU Replacement for Fast Private Network Inference Hongwu Peng Shaoyi Huang Tong Zhou Yukui Luo Chenghong Wang ... Tony Geng Kaleel Mahmood Wujie Wen Xiaolin Xu Caiwen Ding OffRL 47 38 0 20 Aug 2023
Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment Jie Zhu Leye Wang Xiao Han 33 9 0 11 Aug 2022
A Blessing of Dimensionality in Membership Inference through Regularization Jasper Tan Daniel LeJeune Blake Mason Hamid Javadi Richard G. Baraniuk 32 18 0 27 May 2022
A Secure and Efficient Federated Learning Framework for NLP Jieren Deng Chenghong Wang Xianrui Meng Yijue Wang Ji Li Sheng Lin Shuo Han Fei Miao Sanguthevar Rajasekaran Caiwen Ding FedML 77 22 0 28 Jan 2022
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 35 412 0 14 Mar 2021
What is the State of Neural Network Pruning? Davis W. Blalock Jose Javier Gonzalez Ortiz Jonathan Frankle John Guttag 191 1,032 0 06 Mar 2020