Debugging Differential Privacy: A Case Study for Privacy Auditing

24 February 2022

Papers citing "Debugging Differential Privacy: A Case Study for Privacy Auditing"

29 / 29 papers shown

Title
Privacy Auditing of Large Language Models Ashwinee Panda Xinyu Tang Milad Nasr Christopher A. Choquette-Choo Prateek Mittal PILM 62 5 0 09 Mar 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
On Active Privacy Auditing in Supervised Fine-tuning for White-Box Language Models Qian Sun Hanpeng Wu Xi Sheryl Zhang 36 0 0 11 Nov 2024
NeurIPS 2023 Competition: Privacy Preserving Federated Learning Document VQA Marlon Tobaben Mohamed Ali Souibgui Rubèn Pérez Tito Khanh Nguyen Raouf Kerkouche ... Josep Lladós Ernest Valveny Antti Honkela Mario Fritz Dimosthenis Karatzas FedML 39 0 0 06 Nov 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 28 7 0 08 Oct 2024
Models Matter: Setting Accurate Privacy Expectations for Local and Central Differential Privacy Mary Anne Smart Priyanka Nanayakkara Rachel Cummings Gabriel Kaptchuk Elissa M. Redmiles 28 1 0 16 Aug 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
Evaluating Google's Protected Audience Protocol Minjun Long David Evans 24 0 0 13 May 2024
Bridging Quantum Computing and Differential Privacy: Insights into Quantum Computing Privacy Yusheng Zhao Hui Zhong Xinyue Zhang Yuqing Li Chi Zhang Miao Pan 31 3 0 14 Mar 2024
Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting Lisa Oakley Steven Holtzen Alina Oprea 30 0 0 26 Feb 2024
Auditing Private Prediction Karan Chadha Matthew Jagielski Nicolas Papernot Christopher A. Choquette-Choo Milad Nasr 30 4 0 14 Feb 2024
On provable privacy vulnerabilities of graph representations Ruofan Wu Guanhua Fang Qiying Pan Mingyang Zhang Tengfei Liu Weiqiang Wang AAML 27 0 0 06 Feb 2024
Membership Inference Attacks on Diffusion Models via Quantile Regression Shuai Tang Zhiwei Steven Wu Sergul Aydore Michael Kearns Aaron Roth 21 14 0 08 Dec 2023
Recent Advances of Differential Privacy in Centralized Deep Learning: A Systematic Survey Lea Demelius Roman Kern Andreas Trügler SyDa FedML 32 6 0 28 Sep 2023
Unlocking Accuracy and Fairness in Differentially Private Image Classification Leonard Berrada Soham De J. Shen Jamie Hayes Robert Stanforth David Stutz Pushmeet Kohli Samuel L. Smith Borja Balle 21 13 0 21 Aug 2023
Epsilon*: Privacy Metric for Machine Learning Models Diana M. Negoescu H. González Saad Eddin Al Orjany Jilei Yang Yuliia Lut ... Xinyi Zheng Zachariah Douglas Vidita Nolkha P. Ahammad G. Samorodnitsky 28 2 0 21 Jul 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
Selective Pre-training for Private Fine-tuning Da Yu Sivakanth Gopi Janardhan Kulkarni Zi-Han Lin Saurabh Naik Tomasz Religa Jian Yin Huishuai Zhang 30 19 0 23 May 2023
Privacy Auditing with One (1) Training Run Thomas Steinke Milad Nasr Matthew Jagielski 33 76 0 15 May 2023
Have it your way: Individualized Privacy Assignment for DP-SGD Franziska Boenisch Christopher Muhl Adam Dziedzic Roy Rinberg Nicolas Papernot 16 17 0 29 Mar 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 30 52 0 15 Feb 2023
Bounding Training Data Reconstruction in DP-SGD Jamie Hayes Saeed Mahloujifar Borja Balle AAML FedML 33 39 0 14 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
Universal Neural-Cracking-Machines: Self-Configurable Password Models from Auxiliary Data Dario Pasquini G. Ateniese Carmela Troncoso FedML 19 4 0 18 Jan 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 41 35 0 21 Dec 2022
Algorithms with More Granular Differential Privacy Guarantees Badih Ghazi Ravi Kumar Pasin Manurangsi Thomas Steinke 54 6 0 08 Sep 2022
Unlocking High-Accuracy Differentially Private Image Classification through Scale Soham De Leonard Berrada Jamie Hayes Samuel L. Smith Borja Balle 28 217 0 28 Apr 2022
Enhanced Membership Inference Attacks against Machine Learning Models Jiayuan Ye Aadyaa Maddi S. K. Murakonda Vincent Bindschaedler Reza Shokri MIALM MIACV 19 231 0 18 Nov 2021
Variational Bayes In Private Settings (VIPS) Mijung Park James R. Foulds Kamalika Chaudhuri Max Welling 21 42 0 01 Nov 2016