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 Q. Li Yunhui Long Gonzalo Munilla Garrido Chang Ge Bolin Ding David A. Forsyth Bo-wen Li D. Song 52 25 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 31 138 0 08 Feb 2023
LTU Attacker for Membership Inference Joseph Pedersen Rafael Munoz-Gómez Jiangnan Huang Haozhe Sun Wei-Wei Tu Isabelle M Guyon 27 1 0 04 Feb 2022
SHAPr: An Efficient and Versatile Membership Privacy Risk Metric for Machine Learning Vasisht Duddu S. Szyller Nadarajah Asokan 11 12 0 04 Dec 2021
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary Jean-Francois Rajotte S. Mukherjee Caleb Robinson Anthony Ortiz Christopher West J. L. Ferres R. Ng FedML MedIm 122 40 0 18 Jan 2021