GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative
Models

GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models

9 September 2019

Mario Fritz

Papers citing "GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models"

9 / 9 papers shown

Title
On the Privacy Properties of GAN-generated Samples Zinan Lin Vyas Sekar Giulia Fanti PICV 24 26 0 03 Jun 2022
Survey: Leakage and Privacy at Inference Time Marija Jegorova Chaitanya Kaul Charlie Mayor Alison Q. OÑeil Alexander Weir Roderick Murray-Smith Sotirios A. Tsaftaris PILM MIACV 23 71 0 04 Jul 2021
Membership Inference Attacks are Easier on Difficult Problems Avital Shafran Shmuel Peleg Yedid Hoshen MIACV 16 16 0 15 Feb 2021
Differentially Private Synthetic Data: Applied Evaluations and Enhancements Lucas Rosenblatt Xiao-Yang Liu Samira Pouyanfar Eduardo de Leon Anuj M. Desai Joshua Allen SyDa 21 64 0 11 Nov 2020
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models Yixi Xu Soumendu Sundar Mukherjee Xiyang Liu Shruti Tople Rahul Dodhia J. L. Ferres MIACV 24 11 0 11 Sep 2020
A Survey of Privacy Attacks in Machine Learning M. Rigaki Sebastian Garcia PILM AAML 39 213 0 15 Jul 2020
When Machine Unlearning Jeopardizes Privacy Min Chen Zhikun Zhang Tianhao Wang Michael Backes Mathias Humbert Yang Zhang MIACV 31 217 0 05 May 2020
Systematic Evaluation of Privacy Risks of Machine Learning Models Liwei Song Prateek Mittal MIACV 196 359 0 24 Mar 2020
Generating Multi-label Discrete Patient Records using Generative Adversarial Networks Edward Choi Siddharth Biswal B. Malin J. Duke Walter F. Stewart Jimeng Sun SyDa GAN 156 569 0 19 Mar 2017