computer-vision

Membership Inference Attack

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

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Use these libraries to find membership-inference-attack models and implementations

privacytrustlab/ml_privacy_meter

2 papers 534

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Most implemented papers

ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models

A. Salem, M. Backes, Yang Zhang, Mario Fritz, Mathias Humbert•Sun Jun 03 2018

This most comprehensive study so far on this emerging and developing threat using eight diverse datasets which show the viability of the proposed attacks across domains and proposes the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.

1085

Content

Koukyosyumei/AIJack

2 papers 310

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Synthesis of Realistic ECG using Generative Adversarial Networks

T. Ward, Anne Marie Delaney, Eoin Brophy•Wed Sep 18 2019

For the first time, it is both quantitatively and qualitatively demonstrate that GAN architecture can successfully generate time series signals that are not only structurally similar to the training sets but also diverse in nature across generated samples.

97 0

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Membership Inference Attacks on Machine Learning: A Survey

Hongsheng Hu, Z. Salcic, Lichao Sun, G. Dobbie, P. Yu, Xuyun Zhang•Sat Mar 13 2021

This article provides the taxonomies for both attacks and defenses, based on their characterizations, and discusses their pros and cons, and point out several promising future research directions to inspire the researchers who wish to follow this area.

556 0

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Disparate Vulnerability to Membership Inference Attacks

Bogdan Kulynych, Mohammad Yaghini, Giovanni Cherubin, Michael Veale, C. Troncoso•Sat Jun 01 2019

This paper establishes necessary and sufficient conditions for MIAs to be prevented, both on average and for population subgroups, and derives connections of disparate vulnerability to algorithmic fairness and to differential privacy, and establishes which attacks are suitable for estimating disparate vulnerability.

50 0

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Understanding Membership Inferences on Well-Generalized Learning Models

Yunhui Long, Vincent Bindschaedler, Lei Wang, Diyue Bu, Xiaofeng Wang, Haixu Tang, Carl A. Gunter, Kai Chen•Mon Feb 12 2018

It is demonstrated that even a well-generalized model contains vulnerable instances subject to a new generalized MIA (GMIA), and novel techniques for selecting vulnerable instances and detecting their subtle influences ignored by overfitting metrics are used.

240 0

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Machine Learning with Membership Privacy using Adversarial Regularization

Milad Nasr, R. Shokri, Amir Houmansadr•Sun Jan 14 2018

It is shown that the min-max strategy can mitigate the risks of membership inference attacks (near random guess), and can achieve this with a negligible drop in the model's prediction accuracy (less than 4%).

528 0

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Safety and Performance, Why not Both? Bi-Objective Optimized Model Compression toward AI Software Deployment

Jie Zhu, Leye Wang, Xiao Han•Wed Aug 10 2022

A concrete safe model compression mechanism, called MIA-SafeCompress, which can automatically compress a big model to a small one following the dynamic sparse training paradigm, inspired by the test-driven development (TDD) paradigm in software engineering is proposed.

14 0

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MemGuard: Defending against Black-Box Membership Inference Attacks via Adversarial Examples

M. Backes, Yang Zhang, N. Gong, Jinyuan Jia, Ahmed Salem•Sun Sep 22 2019

This work proposes MemGuard, the first defense with formal utility-loss guarantees against black-box membership inference attacks and is the first one to show that adversarial examples can be used as defensive mechanisms to defend against membership inference attack.

445 0

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Membership Inference Attacks From First Principles

Florian Tramèr, Milad Nasr, Nicholas Carlini, Shuang Song, Steve Chien, A. Terzis•Mon Dec 06 2021

A Likelihood Ratio Attack is developed that is $10\times more powerful at low false-positive rates, and also strictly dominates prior attacks on existing metrics.

950 0

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Membership Inference Attacks Against Machine Learning Models

R. Shokri, Vitaly Shmatikov, M. Stronati, Congzheng Song•Mon Oct 17 2016

This work quantitatively investigates how machine learning models leak information about the individual data records on which they were trained and empirically evaluates the inference techniques on classification models trained by commercial "machine learning as a service" providers such as Google and Amazon.

4866 0

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