Privacy Risk in Machine Learning: Analyzing the Connection to
Overfitting

Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting

5 September 2017

Irene Giacomelli

Matt Fredrikson

Papers citing "Privacy Risk in Machine Learning: Analyzing the Connection to Overfitting"

10 / 10 papers shown

Title
Machine Unlearning in Contrastive Learning Zixin Wang Kongyang Chen MU SSL 16 0 0 12 May 2024
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 96 167 0 01 Mar 2023
Understanding Rare Spurious Correlations in Neural Networks Yao-Yuan Yang Chi-Ning Chou Kamalika Chaudhuri AAML 23 25 0 10 Feb 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
Privacy Assessment of Federated Learning using Private Personalized Layers T. Jourdan A. Boutet Carole Frindel FedML 47 7 0 15 Jun 2021
Membership Inference Attacks on Sequence-to-Sequence Models: Is My Data In Your Machine Translation System? Sorami Hisamoto Matt Post Kevin Duh MIACV SLR 28 106 0 11 Apr 2019
A Fully Private Pipeline for Deep Learning on Electronic Health Records Edward Chou Thao Nguyen Josh Beal Albert Haque Li Fei-Fei SyDa FedML 16 6 0 25 Nov 2018
ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models A. Salem Yang Zhang Mathias Humbert Pascal Berrang Mario Fritz Michael Backes MIACV MIALM 36 928 0 04 Jun 2018
Performing Co-Membership Attacks Against Deep Generative Models Kin Sum Liu Chaowei Xiao Bo-wen Li Jie Gao AAML MIACV 18 58 0 24 May 2018
The Power of Linear Reconstruction Attacks S. Kasiviswanathan M. Rudelson Adam D. Smith AAML 57 54 0 08 Oct 2012