SoK: Unintended Interactions among Machine Learning Defenses and Risks

7 December 2023

Papers citing "SoK: Unintended Interactions among Machine Learning Defenses and Risks"

50 / 93 papers shown

Title
Purifier: Defending Data Inference Attacks via Transforming Confidence Scores Ziqi Yang Li-Juan Wang D. Yang Jie Wan Ziming Zhao E. Chang Fan Zhang Kui Ren AAML 51 15 0 01 Dec 2022
Fairness Increases Adversarial Vulnerability Cuong Tran Keyu Zhu Ferdinando Fioretto Pascal Van Hentenryck 48 6 0 21 Nov 2022
Differentially Private Optimizers Can Learn Adversarially Robust Models Yuan Zhang Zhiqi Bu 51 3 0 16 Nov 2022
Membership Inference Attacks and Generalization: A Causal Perspective Teodora Baluta Shiqi Shen S. Hitarth Shruti Tople Prateek Saxena OOD MIACV 65 19 0 18 Sep 2022
Distribution inference risks: Identifying and mitigating sources of leakage Valentin Hartmann Léo Meynent Maxime Peyrard Dimitrios Dimitriadis Shruti Tople Robert West MIACV 37 14 0 18 Sep 2022
Survey on Fairness Notions and Related Tensions Guilherme Alves Fabien Bernier Miguel Couceiro K. Makhlouf C. Palamidessi Sami Zhioua FaML 75 26 0 16 Sep 2022
Model Inversion Attacks against Graph Neural Networks Zaixin Zhang Qi Liu Zhenya Huang Hao Wang Cheekong Lee Enhong AAML 61 36 0 16 Sep 2022
Are Attribute Inference Attacks Just Imputation? Bargav Jayaraman David Evans TDI MIACV 68 49 0 02 Sep 2022
Inferring Sensitive Attributes from Model Explanations Vasisht Duddu A. Boutet MIACV SILM 67 17 0 21 Aug 2022
Careful What You Wish For: on the Extraction of Adversarially Trained Models Kacem Khaled Gabriela Nicolescu F. Magalhães MIACV AAML 45 4 0 21 Jul 2022
Protecting Global Properties of Datasets with Distribution Privacy Mechanisms Michelle Chen O. Ohrimenko FedML 41 12 0 18 Jul 2022
The Privacy Onion Effect: Memorization is Relative Nicholas Carlini Matthew Jagielski Chiyuan Zhang Nicolas Papernot Andreas Terzis Florian Tramèr PILM MIACV 114 109 0 21 Jun 2022
On the Role of Generalization in Transferability of Adversarial Examples Yilin Wang Farzan Farnia AAML 50 11 0 18 Jun 2022
DualCF: Efficient Model Extraction Attack from Counterfactual Explanations Yongjie Wang Hangwei Qian Chunyan Miao AAML 40 31 0 13 May 2022
The Road to Explainability is Paved with Bias: Measuring the Fairness of Explanations Aparna Balagopalan Haoran Zhang Kimia Hamidieh Thomas Hartvigsen Frank Rudzicz Marzyeh Ghassemi 69 78 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 94 120 0 31 Mar 2022
Fingerprinting Deep Neural Networks Globally via Universal Adversarial Perturbations Zirui Peng Shaofeng Li Guoxing Chen Cheng Zhang Haojin Zhu Minhui Xue AAML FedML 55 68 0 17 Feb 2022
Quantifying Memorization Across Neural Language Models Nicholas Carlini Daphne Ippolito Matthew Jagielski Katherine Lee Florian Tramèr Chiyuan Zhang PILM 121 620 0 15 Feb 2022
Defending against Reconstruction Attacks with Rényi Differential Privacy Pierre Stock I. Shilov Ilya Mironov Alexandre Sablayrolles AAML SILM MIACV 63 40 0 15 Feb 2022
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification Yuxin Wen Jonas Geiping Liam H. Fowl Micah Goldblum Tom Goldstein FedML 180 96 0 01 Feb 2022
Understanding Why Generalized Reweighting Does Not Improve Over ERM Runtian Zhai Chen Dan Zico Kolter Pradeep Ravikumar OOD 56 27 0 28 Jan 2022
Are Your Sensitive Attributes Private? Novel Model Inversion Attribute Inference Attacks on Classification Models Shagufta Mehnaz S. V. Dibbo Ehsanul Kabir Ninghui Li E. Bertino MIACV 65 62 0 23 Jan 2022
Counterfactual Memorization in Neural Language Models Chiyuan Zhang Daphne Ippolito Katherine Lee Matthew Jagielski Florian Tramèr Nicholas Carlini 71 134 0 24 Dec 2021
Towards Understanding the Impact of Model Size on Differential Private Classification Yinchen Shen Zhiguo Wang Ruoyu Sun Xiaojing Shen 62 10 0 27 Nov 2021
A Fairness Analysis on Private Aggregation of Teacher Ensembles Cuong Tran M. H. Dinh Kyle Beiter Ferdinando Fioretto 50 12 0 17 Sep 2021
A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning Yehuda Dar Vidya Muthukumar Richard G. Baraniuk 77 72 0 06 Sep 2021
LinkTeller: Recovering Private Edges from Graph Neural Networks via Influence Analysis Fan Wu Yunhui Long Ce Zhang Yue Liu AAML 69 97 0 14 Aug 2021
Deduplicating Training Data Makes Language Models Better Katherine Lee Daphne Ippolito A. Nystrom Chiyuan Zhang Douglas Eck Chris Callison-Burch Nicholas Carlini SyDa 360 628 0 14 Jul 2021
Accuracy, Interpretability, and Differential Privacy via Explainable Boosting Harsha Nori R. Caruana Zhiqi Bu J. Shen Janardhan Kulkarni 65 38 0 17 Jun 2021
CRFL: Certifiably Robust Federated Learning against Backdoor Attacks Chulin Xie Minghao Chen Pin-Yu Chen Yue Liu FedML 75 169 0 15 Jun 2021
Differentially Empirical Risk Minimization under the Fairness Lens Cuong Tran My H. Dinh Ferdinando Fioretto 35 46 0 04 Jun 2021
Counterfactual Explanations Can Be Manipulated Dylan Slack Sophie Hilgard Himabindu Lakkaraju Sameer Singh 64 137 0 04 Jun 2021
Fooling Partial Dependence via Data Poisoning Hubert Baniecki Wojciech Kretowicz P. Biecek AAML 47 23 0 26 May 2021
Honest-but-Curious Nets: Sensitive Attributes of Private Inputs Can Be Secretly Coded into the Classifiers' Outputs Mohammad Malekzadeh Anastasia Borovykh Deniz Gündüz MIACV 64 42 0 25 May 2021
Exploiting Explanations for Model Inversion Attacks Xu Zhao Wencan Zhang Xiao Xiao Brian Y. Lim MIACV 64 83 0 26 Apr 2021
Membership Inference Attacks on Machine Learning: A Survey Hongsheng Hu Z. Salcic Lichao Sun Gillian Dobbie Philip S. Yu Xuyun Zhang MIACV 114 435 0 14 Mar 2021
ML-Doctor: Holistic Risk Assessment of Inference Attacks Against Machine Learning Models Yugeng Liu Rui Wen Xinlei He A. Salem Zhikun Zhang Michael Backes Emiliano De Cristofaro Mario Fritz Yang Zhang AAML 62 132 0 04 Feb 2021
Property Inference From Poisoning Melissa Chase Esha Ghosh Saeed Mahloujifar MIACV 73 80 0 26 Jan 2021
Exacerbating Algorithmic Bias through Fairness Attacks Ninareh Mehrabi Muhammad Naveed Fred Morstatter Aram Galstyan AAML 65 67 0 16 Dec 2020
Robustness Threats of Differential Privacy Nurislam Tursynbek Aleksandr Petiushko Ivan Oseledets AAML 70 14 0 14 Dec 2020
Extracting Training Data from Large Language Models Nicholas Carlini Florian Tramèr Eric Wallace Matthew Jagielski Ariel Herbert-Voss ... Tom B. Brown D. Song Ulfar Erlingsson Alina Oprea Colin Raffel MLAU SILM 489 1,917 0 14 Dec 2020
When is Memorization of Irrelevant Training Data Necessary for High-Accuracy Learning? Gavin Brown Mark Bun Vitaly Feldman Adam D. Smith Kunal Talwar 292 99 0 11 Dec 2020
FairOD: Fairness-aware Outlier Detection Shubhranshu Shekhar Neil Shah Leman Akoglu 66 37 0 05 Dec 2020
Empirical observation of negligible fairness-accuracy trade-offs in machine learning for public policy Kit T. Rodolfa Hemank Lamba Rayid Ghani 77 90 0 05 Dec 2020
On the Privacy Risks of Algorithmic Fairness Hong Chang Reza Shokri FaML 172 112 0 07 Nov 2020
Robustness May Be at Odds with Fairness: An Empirical Study on Class-wise Accuracy Philipp Benz Chaoning Zhang Adil Karjauv In So Kweon AAML 59 59 0 26 Oct 2020
RobustBench: a standardized adversarial robustness benchmark Francesco Croce Maksym Andriushchenko Vikash Sehwag Edoardo Debenedetti Nicolas Flammarion M. Chiang Prateek Mittal Matthias Hein VLM 320 702 0 19 Oct 2020
To be Robust or to be Fair: Towards Fairness in Adversarial Training Han Xu Xiaorui Liu Yaxin Li Anil K. Jain Jiliang Tang 46 180 0 13 Oct 2020
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning Vasisht Duddu A. Boutet Virat Shejwalkar GNN 39 4 0 02 Oct 2020
Model extraction from counterfactual explanations Ulrich Aïvodji Alexandre Bolot Sébastien Gambs MIACV MLAU 60 51 0 03 Sep 2020