Privacy Analysis of Deep Learning in the Wild: Membership Inference
Attacks against Transfer Learning

Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning

10 September 2020

Michael Backes

Papers citing "Privacy Analysis of Deep Learning in the Wild: Membership Inference Attacks against Transfer Learning"

9 / 9 papers shown

Title
Rethinking Membership Inference Attacks Against Transfer Learning Yanwei Yue Jing Chen Qianru Fang Kun He Ziming Zhao Hao Ren Guowen Xu Yang Liu Yang Xiang 66 34 0 20 Jan 2025
Hiding in Plain Sight: Differential Privacy Noise Exploitation for Evasion-resilient Localized Poisoning Attacks in Multiagent Reinforcement Learning Md Tamjid Hossain Hung M. La AAML 18 0 0 01 Jul 2023
On the Impossible Safety of Large AI Models El-Mahdi El-Mhamdi Sadegh Farhadkhani R. Guerraoui Nirupam Gupta L. Hoang Rafael Pinot Sébastien Rouault John Stephan 37 31 0 30 Sep 2022
Federated and Transfer Learning: A Survey on Adversaries and Defense Mechanisms Ehsan Hallaji R. Razavi-Far M. Saif AAML FedML 27 13 0 05 Jul 2022
Commonality in Natural Images Rescues GANs: Pretraining GANs with Generic and Privacy-free Synthetic Data Kyungjune Baek Hyunjung Shim 32 12 0 11 Apr 2022
Membership Inference Attacks and Defenses in Neural Network Pruning Xiaoyong Yuan Lan Zhang AAML 24 44 0 07 Feb 2022
Enhanced Membership Inference Attacks against Machine Learning Models Jiayuan Ye Aadyaa Maddi S. K. Murakonda Vincent Bindschaedler Reza Shokri MIALM MIACV 27 233 0 18 Nov 2021
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
TransMIA: Membership Inference Attacks Using Transfer Shadow Training Seira Hidano Takao Murakami Yusuke Kawamoto MIACV 30 13 0 30 Nov 2020