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Inference Attack

4 papers with code ยท Adversarial

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Eavesdrop the Composition Proportion of Training Labels in Federated Learning

14 Oct 2019

Federated learning (FL) has recently emerged as a new form of collaborative machine learning, where a common model can be learned while keeping all the training data on local devices.

INFERENCE ATTACK

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

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.

INFERENCE ATTACK

Synthesis of Realistic ECG using Generative Adversarial Networks

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.

INFERENCE ATTACK TIME SERIES

Defending against Machine Learning based Inference Attacks via Adversarial Examples: Opportunities and Challenges

17 Sep 2019

To defend against inference attacks, we can add carefully crafted noise into the public data to turn them into adversarial examples, such that attackers' classifiers make incorrect predictions for the private data.

INFERENCE ATTACK

GAN-Leaks: A Taxonomy of Membership Inference Attacks against GANs

9 Sep 2019

In this paper, we focus on membership inference attack against GANs that has the potential to reveal information about victim models' training data.

INFERENCE ATTACK

Adversarial Privacy Preservation under Attribute Inference Attack

19 Jun 2019

With the prevalence of machine learning services, crowdsourced data containing sensitive information poses substantial privacy challenges.

INFERENCE ATTACK REPRESENTATION LEARNING

Reconciling Utility and Membership Privacy via Knowledge Distillation

15 Jun 2019

In this work, we present a new defense against membership inference attacks that preserves the utility of the target machine learning models significantly better than prior defenses.

INFERENCE ATTACK MODEL COMPRESSION

Reconstruction and Membership Inference Attacks against Generative Models

7 Jun 2019

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

DENSITY ESTIMATION INFERENCE ATTACK

Disparate Vulnerability: on the Unfairness of Privacy Attacks Against Machine Learning

2 Jun 2019

A membership inference attack (MIA) against a machine learning model enables an attacker to determine whether a given data record was part of the model's training dataset or not.

INFERENCE ATTACK