Assessing differentially private deep learning with Membership Inference

Assessing differentially private deep learning with Membership Inference

24 December 2019

Philip-William Grassal

Florian Kerschbaum

Papers citing "Assessing differentially private deep learning with Membership Inference"

12 / 12 papers shown

Title
Semantic Membership Inference Attack against Large Language Models Hamid Mozaffari Virendra J. Marathe MIALM 53 3 0 14 Jun 2024
PrivatEyes: Appearance-based Gaze Estimation Using Federated Secure Multi-Party Computation Mayar Elfares Pascal Reisert Zhiming Hu Wenwu Tang Ralf Küsters Andreas Bulling FedML 26 4 0 29 Feb 2024
Exploring the Limits of Differentially Private Deep Learning with Group-wise Clipping Jiyan He Xuechen Li Da Yu Huishuai Zhang Janardhan Kulkarni Y. Lee A. Backurs Nenghai Yu Jiang Bian 30 46 0 03 Dec 2022
Individual Privacy Accounting for Differentially Private Stochastic Gradient Descent Da Yu Gautam Kamath Janardhan Kulkarni Tie-Yan Liu Jian Yin Huishuai Zhang 19 17 0 06 Jun 2022
Mixed Differential Privacy in Computer Vision Aditya Golatkar Alessandro Achille Yu-Xiang Wang Aaron Roth Michael Kearns Stefano Soatto PICV VLM 20 49 0 22 Mar 2022
Privacy-Preserving Machine Learning: Methods, Challenges and Directions Runhua Xu Nathalie Baracaldo J. Joshi 32 100 0 10 Aug 2021
Responsible and Regulatory Conform Machine Learning for Medicine: A Survey of Challenges and Solutions Eike Petersen Yannik Potdevin Esfandiar Mohammadi Stephan Zidowitz Sabrina Breyer ... Sandra Henn Ludwig Pechmann M. Leucker P. Rostalski Christian Herzog FaML AILaw OOD 29 21 0 20 Jul 2021
Large Scale Private Learning via Low-rank Reparametrization Da Yu Huishuai Zhang Wei Chen Jian Yin Tie-Yan Liu 23 100 0 17 Jun 2021
Can Differential Privacy Practically Protect Collaborative Deep Learning Inference for the Internet of Things? Jihyeon Ryu Yifeng Zheng Yansong Gao A. Abuadbba Junyaup Kim Dongho Won Surya Nepal Hyoungshick Kim Cong Wang 18 12 0 08 Apr 2021
Quantifying identifiability to choose and audit $ε$ in differentially private deep learning Daniel Bernau Günther Eibl Philip-William Grassal Hannah Keller Florian Kerschbaum FedML 14 5 0 04 Mar 2021
Do Not Let Privacy Overbill Utility: Gradient Embedding Perturbation for Private Learning Da Yu Huishuai Zhang Wei Chen Tie-Yan Liu FedML SILM 94 110 0 25 Feb 2021
Hide-and-Seek Privacy Challenge James Jordon Daniel Jarrett Jinsung Yoon Tavian Barnes Paul Elbers P. Thoral A. Ercole Cheng Zhang Danielle Belgrave M. Schaar MIACV 14 24 0 23 Jul 2020