A General Framework for Auditing Differentially Private Machine Learning

16 October 2022

Elliott Zaresky-Williams

Papers citing "A General Framework for Auditing Differentially Private Machine Learning"

27 / 27 papers shown

Title
MCMC for Bayesian estimation of Differential Privacy from Membership Inference Attacks Ceren Yildirim Kamer Kaya Sinan Yildirim Erkay Savas 31 0 0 23 Apr 2025
Stop Walking in Circles! Bailing Out Early in Projected Gradient Descent Philip Doldo Derek Everett Amol Khanna A. Nguyen Edward Raff AAML 46 0 0 25 Mar 2025
Curator Attack: When Blackbox Differential Privacy Auditing Loses Its Power Shiming Wang Liyao Xiang Bowei Cheng Zhe Ji Tianran Sun Xinbing Wang 69 0 0 25 Nov 2024
Position: Challenges and Opportunities for Differential Privacy in the U.S. Federal Government Amol Khanna Adam McCormick A. Nguyen Chris Aguirre Edward Raff 26 0 0 21 Oct 2024
VFLGAN-TS: Vertical Federated Learning-based Generative Adversarial Networks for Publication of Vertically Partitioned Time-Series Data Xun Yuan Zilong Zhao P. Gope Biplab Sikdar AI4TS 25 1 0 05 Sep 2024
High-Dimensional Distributed Sparse Classification with Scalable Communication-Efficient Global Updates Fred Lu Ryan R. Curtin Edward Raff Francis Ferraro James Holt 18 1 0 08 Jul 2024
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model Tudor Cebere A. Bellet Nicolas Papernot 30 9 0 23 May 2024
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication Xun Yuan Yang Yang P. Gope A. Pasikhani Biplab Sikdar 29 2 0 15 Apr 2024
Revisiting Differentially Private Hyper-parameter Tuning Zihang Xiang Tianhao Wang Cheng-Long Wang Di Wang 34 6 0 20 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 34 8 0 12 Feb 2024
PrivLM-Bench: A Multi-level Privacy Evaluation Benchmark for Language Models Haoran Li Dadi Guo Donghao Li Wei Fan Qi Hu Xin Liu Chunkit Chan Duanyi Yao Yuan Yao Yangqiu Song PILM 29 24 0 07 Nov 2023
Scaling Up Differentially Private LASSO Regularized Logistic Regression via Faster Frank-Wolfe Iterations Edward Raff Amol Khanna Fred Lu 15 6 0 30 Oct 2023
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective Héber H. Arcolezi Sébastien Gambs 32 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 28 2 0 21 Jul 2023
DP-Auditorium: a Large Scale Library for Auditing Differential Privacy William Kong Andrés Munoz Medina Mónica Ribero Umar Syed 24 2 0 10 Jul 2023
Probing the Transition to Dataset-Level Privacy in ML Models Using an Output-Specific and Data-Resolved Privacy Profile Tyler LeBlond Joseph Munoz Fred Lu Maya Fuchs Elliott Zaresky-Williams Edward Raff Brian Testa 14 3 0 27 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 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
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
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
Bayesian Estimation of Differential Privacy Santiago Zanella Béguelin Lukas Wutschitz Shruti Tople A. Salem Victor Rühle Andrew J. Paverd Mohammad Naseri Boris Köpf Daniel Jones 6 36 0 10 Jun 2022
Opacus: User-Friendly Differential Privacy Library in PyTorch Ashkan Yousefpour I. Shilov Alexandre Sablayrolles Davide Testuggine Karthik Prasad ... Sayan Gosh Akash Bharadwaj Jessica Zhao Graham Cormode Ilya Mironov VLM 152 349 0 25 Sep 2021
Bounding Information Leakage in Machine Learning Ganesh Del Grosso Georg Pichler C. Palamidessi Pablo Piantanida MIACV FedML 40 10 0 09 May 2021