Inference Attack

40 papers with code • 0 benchmarks • 2 datasets

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Most implemented papers

Membership Inference Attacks against Machine Learning Models

spring-epfl/mia 18 Oct 2016

We quantitatively investigate how machine learning models leak information about the individual data records on which they were trained.

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

AhmedSalem2/ML-Leaks 4 Jun 2018

In addition, we propose the first effective defense mechanisms against such broader class of membership inference attacks that maintain a high level of utility of the ML model.

Synthesis of Realistic ECG using Generative Adversarial Networks

Brophy-E/ECG_GAN_MBD 19 Sep 2019

Finally, we discuss the privacy concerns associated with sharing synthetic data produced by GANs and test their ability to withstand a simple membership inference attack.

Disparate Vulnerability to Membership Inference Attacks

spring-epfl/disparate-vulnerability 2 Jun 2019

Differential privacy bounds disparate vulnerability but can significantly reduce the accuracy of the model.

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

jjy1994/MemGuard 23 Sep 2019

Specifically, given a black-box access to the target classifier, the attacker trains a binary classifier, which takes a data sample's confidence score vector predicted by the target classifier as an input and predicts the data sample to be a member or non-member of the target classifier's training dataset.

Formalizing and Estimating Distribution Inference Risks

iamgroot42/formestdistrisks 13 Sep 2021

Distribution inference, sometimes called property inference, infers statistical properties about a training set from access to a model trained on that data.

Understanding Membership Inferences on Well-Generalized Learning Models

BielStela/membership_inference 13 Feb 2018

Membership Inference Attack (MIA) determines the presence of a record in a machine learning model's training data by querying the model.

Machine Learning with Membership Privacy using Adversarial Regularization

hyhmia/BlindMI 16 Jul 2018

In this paper, we focus on such attacks against black-box models, where the adversary can only observe the output of the model, but not its parameters.

Privacy Risks of Securing Machine Learning Models against Adversarial Examples

inspire-group/privacy-vs-robustness 24 May 2019

To perform the membership inference attacks, we leverage the existing inference methods that exploit model predictions.

GAN-Leaks: A Taxonomy of Membership Inference Attacks against Generative Models

DingfanChen/GAN-Leaks 9 Sep 2019

In addition, we propose the first generic attack model that can be instantiated in a large range of settings and is applicable to various kinds of deep generative models.