Membership Inference Attack

29 papers with code • 0 benchmarks • 0 datasets

This task has no description! Would you like to contribute one?


Use these libraries to find Membership Inference Attack models and implementations

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.

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.

Reconstruction and Membership Inference Attacks against Generative Models

SAP-samples/security-research-membership-inference-against-generative-networks 7 Jun 2019

We present two information leakage attacks that outperform previous work on membership inference against generative models.

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.

An Empirical Study on the Intrinsic Privacy of SGD

microsoft/intrinsic-private-sgd 5 Dec 2019

Introducing noise in the training of machine learning systems is a powerful way to protect individual privacy via differential privacy guarantees, but comes at a cost to utility.