Nearly Tight Black-Box Auditing of Differentially Private Machine
Learning

Nearly Tight Black-Box Auditing of Differentially Private Machine Learning

23 May 2024

Meenatchi Sundaram Muthu Selva Annamalai

Emiliano De Cristofaro

ArXiv (abs)PDF HTML

Papers citing "Nearly Tight Black-Box Auditing of Differentially Private Machine Learning"

16 / 16 papers shown

Title
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis Chen Gong Kecen Li Zinan Lin Tianhao Wang 188 5 0 18 Mar 2025
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 123 0 0 16 Mar 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 165 0 0 02 Dec 2024
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model Tudor Cebere A. Bellet Nicolas Papernot 82 10 0 23 May 2024
Tight Auditing of Differentially Private Machine Learning Milad Nasr Jamie Hayes Thomas Steinke Borja Balle Florian Tramèr Matthew Jagielski Nicholas Carlini Andreas Terzis FedML 62 53 0 15 Feb 2023
One-shot Empirical Privacy Estimation for Federated Learning Galen Andrew Peter Kairouz Sewoong Oh Alina Oprea H. B. McMahan Vinith Suriyakumar FedML 116 35 0 06 Feb 2023
Bayesian Estimation of Differential Privacy Santiago Zanella Béguelin Lukas Wutschitz Shruti Tople A. Salem Victor Rühle Andrew Paverd Mohammad Naseri Boris Köpf Daniel Jones 74 39 0 10 Jun 2022
Debugging Differential Privacy: A Case Study for Privacy Auditing Florian Tramèr Andreas Terzis Thomas Steinke Shuang Song Matthew Jagielski Nicholas Carlini 64 43 0 24 Feb 2022
Membership Inference Attacks From First Principles Nicholas Carlini Steve Chien Milad Nasr Shuang Song Andreas Terzis Florian Tramèr MIACV MIALM 85 704 0 07 Dec 2021
Not all noise is accounted equally: How differentially private learning benefits from large sampling rates Friedrich Dörmann Osvald Frisk L. Andersen Christian Fischer Pedersen FedML 88 25 0 12 Oct 2021
Synthetic Data -- Anonymisation Groundhog Day Theresa Stadler Bristena Oprisanu Carmela Troncoso 75 159 0 13 Nov 2020
Tempered Sigmoid Activations for Deep Learning with Differential Privacy Nicolas Papernot Abhradeep Thakurta Shuang Song Steve Chien Ulfar Erlingsson AAML 187 179 0 28 Jul 2020
Auditing Differentially Private Machine Learning: How Private is Private SGD? Matthew Jagielski Jonathan R. Ullman Alina Oprea FedML 74 245 0 13 Jun 2020
Membership Inference Attacks against Machine Learning Models Reza Shokri M. Stronati Congzheng Song Vitaly Shmatikov SLR MIALM MIACV 261 4,135 0 18 Oct 2016
Deep Learning with Differential Privacy Martín Abadi Andy Chu Ian Goodfellow H. B. McMahan Ilya Mironov Kunal Talwar Li Zhang FedML SyDa 216 6,130 0 01 Jul 2016
Wide Residual Networks Sergey Zagoruyko N. Komodakis 349 7,985 0 23 May 2016

We use cookies and other tracking technologies to improve your browsing experience on our website, to show you personalized content and targeted ads, to analyze our website traffic, and to understand where our visitors are coming from. See our policy.