SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning

21 December 2022

Papers citing "SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning"

7 / 7 papers shown

Title
The DCR Delusion: Measuring the Privacy Risk of Synthetic Data Zexi Yao Natasa Krco Georgi Ganev Yves-Alexandre de Montjoye 81 0 0 02 May 2025
The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text Matthieu Meeus Lukas Wutschitz Santiago Zanella Béguelin Shruti Tople Reza Shokri 75 0 0 24 Feb 2025
Reconstructing training data from document understanding models Jérémie Dentan Arnaud Paran A. Shabou AAML SyDa 34 1 0 05 Jun 2024
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries Rachel Cummings Shlomi Hod Jayshree Sarathy Marika Swanberg 34 2 0 02 May 2024
State-of-the-Art Approaches to Enhancing Privacy Preservation of Machine Learning Datasets: A Survey Chaoyu Zhang Shaoyu Li AILaw 43 3 0 25 Feb 2024
Verifiable and Provably Secure Machine Unlearning Thorsten Eisenhofer Doreen Riepel Varun Chandrasekaran Esha Ghosh O. Ohrimenko Nicolas Papernot AAML MU 28 25 0 17 Oct 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