Bayesian Estimation of Differential Privacy

10 June 2022

Santiago Zanella Béguelin

Papers citing "Bayesian Estimation of Differential Privacy"

22 / 22 papers shown

Title
How Well Can Differential Privacy Be Audited in One Run? Amit Keinan Moshe Shenfeld Katrina Ligett 61 0 0 10 Mar 2025
The Canary's Echo: Auditing Privacy Risks of LLM-Generated Synthetic Text Matthieu Meeus Lukas Wutschitz Santiago Zanella Béguelin Shruti Tople Reza Shokri 80 0 0 24 Feb 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 85 0 0 02 Dec 2024
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD Thomas Steinke Milad Nasr Arun Ganesh Borja Balle Christopher A. Choquette-Choo Matthew Jagielski Jamie Hayes Abhradeep Thakurta Adam Smith Andreas Terzis 34 7 0 08 Oct 2024
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning Meenatchi Sundaram Muthu Selva Annamalai Emiliano De Cristofaro 39 11 0 23 May 2024
"What do you want from theory alone?" Experimenting with Tight Auditing of Differentially Private Synthetic Data Generation Meenatchi Sundaram Muthu Selva Annamalai Georgi Ganev Emiliano De Cristofaro 35 9 0 16 May 2024
Shake to Leak: Fine-tuning Diffusion Models Can Amplify the Generative Privacy Risk Zhangheng Li Junyuan Hong Bo-wen Li Zhangyang Wang DiffM 38 17 0 14 Mar 2024
Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting Lisa Oakley Steven Holtzen Alina Oprea 35 0 0 26 Feb 2024
Closed-Form Bounds for DP-SGD against Record-level Inference Giovanni Cherubin Boris Köpf Andrew J. Paverd Shruti Tople Lukas Wutschitz Santiago Zanella Béguelin 33 2 0 22 Feb 2024
Revisiting Differentially Private Hyper-parameter Tuning Zihang Xiang Tianhao Wang Cheng-Long Wang Di Wang 34 6 0 20 Feb 2024
PANORAMIA: Privacy Auditing of Machine Learning Models without Retraining Mishaal Kazmi H. Lautraite Alireza Akbari Mauricio Soroco Qiaoyue Tang Tao Wang Sébastien Gambs Mathias Lécuyer 34 8 0 12 Feb 2024
Label Poisoning is All You Need Rishi Jha J. Hayase Sewoong Oh AAML 22 28 0 29 Oct 2023
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD Anvith Thudi Hengrui Jia Casey Meehan Ilia Shumailov Nicolas Papernot 24 3 0 01 Jul 2023
Gaussian Membership Inference Privacy Tobias Leemann Martin Pawelczyk Gjergji Kasneci 22 15 0 12 Jun 2023
A Note On Interpreting Canary Exposure Matthew Jagielski 16 4 0 31 May 2023
Unleashing the Power of Randomization in Auditing Differentially Private ML Krishna Pillutla Galen Andrew Peter Kairouz H. B. McMahan Alina Oprea Sewoong Oh 30 20 0 29 May 2023
Privacy Auditing with One (1) Training Run Thomas Steinke Milad Nasr Matthew Jagielski 33 77 0 15 May 2023
Tight Auditing of Differentially Private Machine Learning Milad Nasr Jamie Hayes Thomas Steinke Borja Balle Florian Tramèr Matthew Jagielski Nicholas Carlini Andreas Terzis FedML 32 52 0 15 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 21 32 0 06 Feb 2023
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning A. Salem Giovanni Cherubin David E. Evans Boris Köpf Andrew J. Paverd Anshuman Suri Shruti Tople Santiago Zanella Béguelin 44 35 0 21 Dec 2022
TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data F. Houssiau James Jordon Samuel N. Cohen Owen Daniel Andrew Elliott James Geddes C. Mole Camila Rangel Smith Lukasz Szpruch 20 45 0 12 Nov 2022
Measuring Forgetting of Memorized Training Examples Matthew Jagielski Om Thakkar Florian Tramèr Daphne Ippolito Katherine Lee ... Eric Wallace Shuang Song Abhradeep Thakurta Nicolas Papernot Chiyuan Zhang TDI 50 102 0 30 Jun 2022