MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models

11 September 2020

Papers citing "MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models"

5 / 5 papers shown

Title
SoK: Privacy-Preserving Data Synthesis Yuzheng Hu Fan Wu Yue Liu Yunhui Long Gonzalo Munilla Garrido Chang Ge Bolin Ding David A. Forsyth Bo-wen Li D. Song 60 26 0 05 Jul 2023
Machine Learning for Synthetic Data Generation: A Review Ying-Cheng Lu Minjie Shen Huazheng Wang Xiao Wang Capucine Van Rechem Tianfan Fu Wenqi Wei SyDa 42 140 0 08 Feb 2023
LTU Attacker for Membership Inference Joseph Pedersen Rafael Munoz-Gómez Jiangnan Huang Haozhe Sun Wei-Wei Tu Isabelle M Guyon 33 1 0 04 Feb 2022
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning Vasisht Duddu S. Szyller Nadarajah Asokan 32 12 0 04 Dec 2021
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary Jean-Francois Rajotte Soumendu Sundar Mukherjee Caleb Robinson Anthony Ortiz Christopher West J. L. Ferres R. Ng FedML MedIm 130 40 0 18 Jan 2021