Tight Auditing of Differentially Private Machine Learning

15 February 2023

Papers citing "Tight Auditing of Differentially Private Machine Learning"

34 / 34 papers shown

Title
Empirical Privacy Variance Yuzheng Hu Fan Wu Ruicheng Xian Yuhang Liu Lydia Zakynthinou Pritish Kamath Chiyuan Zhang David A. Forsyth 62 0 0 16 Mar 2025
$(\varepsilon, δ)$ Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees Juan Felipe Gomez B. Kulynych G. Kaissis Jamie Hayes Borja Balle Antti Honkela 51 0 0 13 Mar 2025
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
General-Purpose $f$ -DP Estimation and Auditing in a Black-Box Setting Önder Askin Holger Dette Martin Dunsche T. Kutta Yun Lu Yu Wei Vassilis Zikas 52 0 0 10 Feb 2025
Safeguarding System Prompts for LLMs Zhifeng Jiang Zhihua Jin Guoliang He AAML SILM 103 1 0 10 Jan 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 28 7 0 08 Oct 2024
Mitigating Noise Detriment in Differentially Private Federated Learning with Model Pre-training Huitong Jin Yipeng Zhou Laizhong Cui Quan Z. Sheng AI4CE 40 0 0 18 Aug 2024
Synthetic Data, Similarity-based Privacy Metrics, and Regulatory (Non-)Compliance Georgi Ganev 32 0 0 24 Jul 2024
Weights Shuffling for Improving DPSGD in Transformer-based Models Jungang Yang Zhe Ji Liyao Xiang 33 0 0 22 Jul 2024
A Benchmark for Multi-speaker Anonymization Xiaoxiao Miao Ruijie Tao Chang Zeng Xin Wang 44 1 0 08 Jul 2024
Attack-Aware Noise Calibration for Differential Privacy B. Kulynych Juan Felipe Gomez G. Kaissis Flavio du Pin Calmon Carmela Troncoso 49 6 0 02 Jul 2024
Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations Vasisht Duddu Oskari Jarvinen Lachlan J. Gunn Nirmal Asokan 64 1 0 25 Jun 2024
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model Tudor Cebere A. Bellet Nicolas Papernot 28 9 0 23 May 2024
Nearly Tight Black-Box Auditing of Differentially Private Machine Learning Meenatchi Sundaram Muthu Selva Annamalai Emiliano De Cristofaro 25 11 0 23 May 2024
Data Contamination Calibration for Black-box LLMs Wen-song Ye Jiaqi Hu Liyao Li Haobo Wang Gang Chen Junbo Zhao 34 6 0 20 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
Bridging Quantum Computing and Differential Privacy: Insights into Quantum Computing Privacy Yusheng Zhao Hui Zhong Xinyue Zhang Yuqing Li Chi Zhang Miao Pan 28 3 0 14 Mar 2024
Visual Privacy Auditing with Diffusion Models Kristian Schwethelm Johannes Kaiser Moritz Knolle Daniel Rueckert Daniel Rueckert Alexander Ziller DiffM AAML 33 0 0 12 Mar 2024
Synthesizing Tight Privacy and Accuracy Bounds via Weighted Model Counting Lisa Oakley Steven Holtzen Alina Oprea 30 0 0 26 Feb 2024
Privacy-Preserving Instructions for Aligning Large Language Models Da Yu Peter Kairouz Sewoong Oh Zheng Xu 32 17 0 21 Feb 2024
Auditing Private Prediction Karan Chadha Matthew Jagielski Nicolas Papernot Christopher A. Choquette-Choo Milad Nasr 30 4 0 14 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 29 8 0 12 Feb 2024
Preserving Node-level Privacy in Graph Neural Networks Zihang Xiang Tianhao Wang Di Wang 23 6 0 12 Nov 2023
Label Poisoning is All You Need Rishi Jha J. Hayase Sewoong Oh AAML 22 28 0 29 Oct 2023
Detecting Pretraining Data from Large Language Models Weijia Shi Anirudh Ajith Mengzhou Xia Yangsibo Huang Daogao Liu Terra Blevins Danqi Chen Luke Zettlemoyer MIALM 23 161 0 25 Oct 2023
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective Héber H. Arcolezi Sébastien Gambs 30 1 0 04 Sep 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 15 2 0 21 Jul 2023
DP-Auditorium: a Large Scale Library for Auditing Differential Privacy William Kong Andrés Munoz Medina Mónica Ribero Umar Syed 13 2 0 10 Jul 2023
Privacy Auditing with One (1) Training Run Thomas Steinke Milad Nasr Matthew Jagielski 25 76 0 15 May 2023
A Randomized Approach for Tight Privacy Accounting Jiachen T. Wang Saeed Mahloujifar Tong Wu R. Jia Prateek Mittal 28 9 0 17 Apr 2023
How to DP-fy ML: A Practical Guide to Machine Learning with Differential Privacy Natalia Ponomareva Hussein Hazimeh Alexey Kurakin Zheng Xu Carson E. Denison H. B. McMahan Sergei Vassilvitskii Steve Chien Abhradeep Thakurta 94 167 0 01 Mar 2023
One-shot Empirical Privacy Estimation for Federated Learning Galen Andrew Peter Kairouz Sewoong Oh Alina Oprea H. B. McMahan Vinith M. Suriyakumar FedML 19 32 0 06 Feb 2023
Private, fair and accurate: Training large-scale, privacy-preserving AI models in medical imaging Soroosh Tayebi Arasteh Alexander Ziller Christiane Kuhl Marcus R. Makowski S. Nebelung R. Braren Daniel Rueckert Daniel Truhn Georgios Kaissis MedIm 32 17 0 03 Feb 2023