Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis
Testing: A Lesson From Fano

Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano

24 October 2022

Alexandre Sablayrolles

Papers citing "Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano"

15 / 15 papers shown

Title
Analyzing Inference Privacy Risks Through Gradients in Machine Learning Zhuohang Li Andrew Lowy Jing Liu T. Koike-Akino K. Parsons Bradley Malin Ye Wang FedML 30 1 0 29 Aug 2024
Differentially Private Representation Learning via Image Captioning Tom Sander Yaodong Yu Maziar Sanjabi Alain Durmus Yi-An Ma Kamalika Chaudhuri Chuan Guo 48 3 0 04 Mar 2024
Measuring Privacy Loss in Distributed Spatio-Temporal Data Tatsuki Koga Casey Meehan Kamalika Chaudhuri 24 0 0 18 Feb 2024
Implicit Bias in Noisy-SGD: With Applications to Differentially Private Training Tom Sander Maxime Sylvestre Alain Durmus 28 1 0 13 Feb 2024
Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks Jiayuan Ye Zhenyu Zhu Fanghui Liu Reza Shokri V. Cevher 27 12 0 31 Oct 2023
Fundamental Limits of Membership Inference Attacks on Machine Learning Models Eric Aubinais Elisabeth Gassiat Pablo Piantanida MIACV 48 2 0 20 Oct 2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification Leonard Berrada Soham De J. Shen Jamie Hayes Robert Stanforth David Stutz Pushmeet Kohli Samuel L. Smith Borja Balle 19 13 0 21 Aug 2023
Bounding data reconstruction attacks with the hypothesis testing interpretation of differential privacy Georgios Kaissis Jamie Hayes Alexander Ziller Daniel Rueckert AAML 28 11 0 08 Jul 2023
Information Flow Control in Machine Learning through Modular Model Architecture Trishita Tiwari Suchin Gururangan Chuan Guo Weizhe Hua Sanjay Kariyappa Udit Gupta Wenjie Xiong Kiwan Maeng Hsien-Hsin S. Lee G. E. Suh 19 6 0 05 Jun 2023
Bounding the Invertibility of Privacy-preserving Instance Encoding using Fisher Information Kiwan Maeng Chuan Guo Sanjay Kariyappa G. E. Suh 11 8 0 06 May 2023
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 94 167 0 01 Mar 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 21 39 0 14 Feb 2023
One-shot Empirical Privacy Estimation for Federated Learning Galen Andrew Peter Kairouz Sewoong Oh Alina Oprea H. B. McMahan Vinith M. Suriyakumar FedML 19 32 0 06 Feb 2023
Bounding Training Data Reconstruction in Private (Deep) Learning Chuan Guo Brian Karrer Kamalika Chaudhuri L. V. D. van der Maaten 103 53 0 28 Jan 2022
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 267 1,808 0 14 Dec 2020