Auditing Differentially Private Machine Learning: How Private is Private SGD?

13 June 2020

Papers citing "Auditing Differentially Private Machine Learning: How Private is Private SGD?"

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Title
Lower Bounds on the MMSE of Adversarially Inferring Sensitive Features Monica Welfert Nathan Stromberg Mario Díaz Lalitha Sankar AAML 29 0 0 13 May 2025
DPImageBench: A Unified Benchmark for Differentially Private Image Synthesis Chen Gong Kecen Li Zinan Lin Tianhao Wang 64 3 0 18 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 85 0 0 24 Feb 2025
Synthetic Data Privacy Metrics Amy Steier Lipika Ramaswamy Andre Manoel Alexa Haushalter 56 1 0 08 Jan 2025
Adversarial Sample-Based Approach for Tighter Privacy Auditing in Final Model-Only Scenarios Sangyeon Yoon Wonje Jeung Albert No 93 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 36 7 0 08 Oct 2024
Membership Inference Attacks Cannot Prove that a Model Was Trained On Your Data Jie Zhang Debeshee Das Gautam Kamath Florian Tramèr MIALM MIACV 238 16 1 29 Sep 2024
QueryCheetah: Fast Automated Discovery of Attribute Inference Attacks Against Query-Based Systems Bozhidar Stevanoski Ana-Maria Cretu Yves-Alexandre de Montjoye AAML 23 1 0 03 Sep 2024
PaCoST: Paired Confidence Significance Testing for Benchmark Contamination Detection in Large Language Models Huixuan Zhang Yun Lin Xiaojun Wan 53 0 0 26 Jun 2024
Laminator: Verifiable ML Property Cards using Hardware-assisted Attestations Vasisht Duddu Oskari Jarvinen Lachlan J. Gunn Nirmal Asokan 74 1 0 25 Jun 2024
VFLGAN: Vertical Federated Learning-based Generative Adversarial Network for Vertically Partitioned Data Publication Xun Yuan Yang Yang P. Gope A. Pasikhani Biplab Sikdar 47 2 0 15 Apr 2024
A Survey of Graph Neural Networks in Real world: Imbalance, Noise, Privacy and OOD Challenges Wei Ju Siyu Yi Yifan Wang Zhiping Xiao Zhengyan Mao ... Senzhang Wang Xinwang Liu Xiao Luo Philip S. Yu Ming Zhang AI4CE 43 36 0 07 Mar 2024
TOFU: A Task of Fictitious Unlearning for LLMs Pratyush Maini Zhili Feng Avi Schwarzschild Zachary Chase Lipton J. Zico Kolter MU CLL 46 146 0 11 Jan 2024
Revealing the True Cost of Locally Differentially Private Protocols: An Auditing Perspective Héber H. Arcolezi Sébastien Gambs 45 1 0 04 Sep 2023
A Survey on Privacy in Graph Neural Networks: Attacks, Preservation, and Applications Yi Zhang Yuying Zhao Zhaoqing Li Xueqi Cheng Yu-Chiang Frank Wang Olivera Kotevska Philip S. Yu Tyler Derr 31 10 0 31 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 37 2 0 21 Jul 2023
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD Anvith Thudi Hengrui Jia Casey Meehan Ilia Shumailov Nicolas Papernot 38 3 0 01 Jul 2023
A Note On Interpreting Canary Exposure Matthew Jagielski 22 4 0 31 May 2023
Training Data Extraction From Pre-trained Language Models: A Survey Shotaro Ishihara 37 46 0 25 May 2023
Auditing and Generating Synthetic Data with Controllable Trust Trade-offs Brian M. Belgodere Pierre Dognin Adam Ivankay Igor Melnyk Youssef Mroueh ... Mattia Rigotti Jerret Ross Yair Schiff Radhika Vedpathak Richard A. Young 34 12 0 21 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 105 167 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 35 52 0 15 Feb 2023
Privacy Risk for anisotropic Langevin dynamics using relative entropy bounds Anastasia Borovykh N. Kantas P. Parpas G. Pavliotis 19 1 0 01 Feb 2023
Extracting Training Data from Diffusion Models Nicholas Carlini Jamie Hayes Milad Nasr Matthew Jagielski Vikash Sehwag Florian Tramèr Borja Balle Daphne Ippolito Eric Wallace DiffM 73 572 0 30 Jan 2023
SoK: Let the Privacy Games Begin! A Unified Treatment of Data Inference Privacy in Machine Learning A. Salem Giovanni Cherubin David Evans Boris Köpf Andrew Paverd Anshuman Suri Shruti Tople Santiago Zanella Béguelin 47 35 0 21 Dec 2022
Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version) Lucas Lange Maja Schneider Peter Christen Erhard Rahm 24 7 0 21 Nov 2022
Provable Membership Inference Privacy Zachary Izzo Jinsung Yoon Sercan Ö. Arik James Zou 44 5 0 12 Nov 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 36 45 0 12 Nov 2022
QuerySnout: Automating the Discovery of Attribute Inference Attacks against Query-Based Systems Ana-Maria Cretu F. Houssiau Antoine Cully Yves-Alexandre de Montjoye AAML 21 10 0 09 Nov 2022
Analyzing Privacy Leakage in Machine Learning via Multiple Hypothesis Testing: A Lesson From Fano Chuan Guo Alexandre Sablayrolles Maziar Sanjabi FedML 29 17 0 24 Oct 2022
Generalised Likelihood Ratio Testing Adversaries through the Differential Privacy Lens Georgios Kaissis Alexander Ziller Stefan Kolek Martinez de Azagra Daniel Rueckert 12 0 0 24 Oct 2022
A General Framework for Auditing Differentially Private Machine Learning Fred Lu Joseph Munoz Maya Fuchs Tyler LeBlond Elliott Zaresky-Williams Edward Raff Francis Ferraro Brian Testa FedML 22 35 0 16 Oct 2022
Differentially Private Deep Learning with ModelMix Hanshen Xiao Jun Wan S. Devadas 29 3 0 07 Oct 2022
CANIFE: Crafting Canaries for Empirical Privacy Measurement in Federated Learning Samuel Maddock Alexandre Sablayrolles Pierre Stock FedML 25 22 0 06 Oct 2022
Algorithms that Approximate Data Removal: New Results and Limitations Vinith Suriyakumar Ashia Wilson MU 49 27 0 25 Sep 2022
Algorithms with More Granular Differential Privacy Guarantees Badih Ghazi Ravi Kumar Pasin Manurangsi Thomas Steinke 69 6 0 08 Sep 2022
Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks Chulin Xie Yunhui Long Pin-Yu Chen Qinbin Li Arash Nourian Sanmi Koyejo Bo Li FedML 55 13 0 08 Sep 2022
SNAP: Efficient Extraction of Private Properties with Poisoning Harsh Chaudhari John Abascal Alina Oprea Matthew Jagielski Florian Tramèr Jonathan R. Ullman MIACV 39 30 0 25 Aug 2022
Private, Efficient, and Accurate: Protecting Models Trained by Multi-party Learning with Differential Privacy Wenqiang Ruan Ming Xu Wenjing Fang Li Wang Lei Wang Wei Han 40 12 0 18 Aug 2022
AlexaTM 20B: Few-Shot Learning Using a Large-Scale Multilingual Seq2Seq Model Saleh Soltan Shankar Ananthakrishnan Jack G. M. FitzGerald Rahul Gupta Wael Hamza ... Mukund Sridhar Fabian Triefenbach Apurv Verma Gokhan Tur Premkumar Natarajan 58 82 0 02 Aug 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 75 102 0 30 Jun 2022
The Privacy Onion Effect: Memorization is Relative Nicholas Carlini Matthew Jagielski Chiyuan Zhang Nicolas Papernot Andreas Terzis Florian Tramèr PILM MIACV 35 102 0 21 Jun 2022
Disparate Impact in Differential Privacy from Gradient Misalignment Maria S. Esipova Atiyeh Ashari Ghomi Yaqiao Luo Jesse C. Cresswell 29 25 0 15 Jun 2022
Neurotoxin: Durable Backdoors in Federated Learning Zhengming Zhang Ashwinee Panda Linyue Song Yaoqing Yang Michael W. Mahoney Joseph E. Gonzalez Kannan Ramchandran Prateek Mittal FedML 40 130 0 12 Jun 2022
Bayesian Estimation of Differential Privacy Santiago Zanella Béguelin Lukas Wutschitz Shruti Tople A. Salem Victor Rühle Andrew Paverd Mohammad Naseri Boris Köpf Daniel Jones 30 36 0 10 Jun 2022
Auditing Differential Privacy in High Dimensions with the Kernel Quantum Rényi Divergence Carles Domingo-Enrich Youssef Mroueh 27 5 0 27 May 2022
How to Combine Membership-Inference Attacks on Multiple Updated Models Matthew Jagielski Stanley Wu Alina Oprea Jonathan R. Ullman Roxana Geambasu 29 10 0 12 May 2022
Synthetic Data -- what, why and how? James Jordon Lukasz Szpruch F. Houssiau M. Bottarelli Giovanni Cherubin Carsten Maple Samuel N. Cohen Adrian Weller 51 109 0 06 May 2022
Truth Serum: Poisoning Machine Learning Models to Reveal Their Secrets Florian Tramèr Reza Shokri Ayrton San Joaquin Hoang Minh Le Matthew Jagielski Sanghyun Hong Nicholas Carlini MIACV 53 109 0 31 Mar 2022
Rethinking Portrait Matting with Privacy Preserving Sihan Ma Jizhizi Li Jing Zhang He-jun Zhang Dacheng Tao 34 23 0 31 Mar 2022