Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?

14 February 2024

Papers citing "Why Does Differential Privacy with Large Epsilon Defend Against Practical Membership Inference Attacks?"

9 / 9 papers shown

Title
Personalized Federated Training of Diffusion Models with Privacy Guarantees Kumar Kshitij Patel Weitong Zhang Lingxiao Wang MedIm 50 0 0 01 Apr 2025
Tokens for Learning, Tokens for Unlearning: Mitigating Membership Inference Attacks in Large Language Models via Dual-Purpose Training Toan Tran Ruixuan Liu Li Xiong MU 41 0 0 27 Feb 2025
A Tale of Two Imperatives: Privacy and Explainability Supriya Manna Niladri Sett 85 0 0 30 Dec 2024
Faster Algorithms for User-Level Private Stochastic Convex Optimization Andrew Lowy Daogao Liu Hilal Asi 18 0 0 24 Oct 2024
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
Private Collaborative Edge Inference via Over-the-Air Computation Selim F. Yilmaz Burak Hasircioglu Li Qiao Deniz Gunduz FedML 48 1 0 30 Jul 2024
Semantic Membership Inference Attack against Large Language Models Hamid Mozaffari Virendra J. Marathe MIALM 45 3 0 14 Jun 2024
Privacy-Preserving Instructions for Aligning Large Language Models Da Yu Peter Kairouz Sewoong Oh Zheng Xu 32 17 0 21 Feb 2024
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