Search Results for author: Fabrice Labeau

Found 11 papers, 1 papers with code

Preserving Privacy in GANs Against Membership Inference Attack

no code implementations6 Nov 2023 Mohammadhadi Shateri, Francisco Messina, Fabrice Labeau, Pablo Piantanida

In the present work, the overfitting in GANs is studied in terms of the discriminator, and a more general measure of overfitting based on the Bhattacharyya coefficient is defined.

Inference Attack Membership Inference Attack +1

$α$-Mutual Information: A Tunable Privacy Measure for Privacy Protection in Data Sharing

no code implementations27 Oct 2023 MirHamed Jafarzadeh Asl, Mohammadhadi Shateri, Fabrice Labeau

This paper adopts Arimoto's $\alpha$-Mutual Information as a tunable privacy measure, in a privacy-preserving data release setting that aims to prevent disclosing private data to adversaries.

Privacy Preserving Time Series

Cardiotocography Signal Abnormality Detection based on Deep Unsupervised Models

no code implementations29 Sep 2022 Julien Bertieaux, Mohammadhadi Shateri, Fabrice Labeau, Thierry Dutoit

The GANomaly framework, modified to capture the underlying distribution of data samples, is used as our main model and is applied to the CTU-UHB dataset.

Anomaly Detection

Learning Sparse Privacy-Preserving Representations for Smart Meters Data

no code implementations17 Jul 2021 Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

We formulate this as the problem of learning a sparse representation of SMs data with minimum information leakage and maximum utility.

Attribute Fault Detection +2

Adversarial Robustness via Fisher-Rao Regularization

1 code implementation12 Jun 2021 Marine Picot, Francisco Messina, Malik Boudiaf, Fabrice Labeau, Ismail Ben Ayed, Pablo Piantanida

Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle.

Adversarial Defense Adversarial Robustness

Deep Directed Information-Based Learning for Privacy-Preserving Smart Meter Data Release

no code implementations20 Nov 2020 Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

In this paper, we study this problem in the context of time series data and smart meters (SMs) power consumption measurements in particular.

Privacy Preserving Time Series +1

On the Impact of Side Information on Smart Meter Privacy-Preserving Methods

no code implementations29 Jun 2020 Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

On the one hand, the releaser in the CAL method, by getting supervision from the actual values of the private variables and feedback from the adversary performance, tries to minimize the adversary log-likelihood.

Privacy Preserving

Privacy-Cost Management in Smart Meters with Mutual Information-Based Reinforcement Learning

no code implementations10 Jun 2020 Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

Unlike previous studies, we model the whole temporal correlation in the data to learn the MI in its general form and use a neural network to estimate the MI-based reward signal to guide the PCMU learning process.

Management Q-Learning +2

Privacy-Preserving Adversarial Network (PPAN) for Continuous non-Gaussian Attributes

no code implementations11 Mar 2020 Mohammadhadi Shateri, Fabrice Labeau

A privacy-preserving adversarial network (PPAN) was recently proposed as an information-theoretical framework to address the issue of privacy in data sharing.

Privacy Preserving

Privacy-Cost Management in Smart Meters Using Deep Reinforcement Learning

no code implementations10 Mar 2020 Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

Smart meters (SMs) play a pivotal rule in the smart grid by being able to report the electricity usage of consumers to the utility provider (UP) almost in real-time.

Management Q-Learning +2

Real-Time Privacy-Preserving Data Release for Smart Meters

no code implementations14 Jun 2019 Mohammadhadi Shateri, Francisco Messina, Pablo Piantanida, Fabrice Labeau

In this paper, we focus on real-time privacy threats, i. e., potential attackers that try to infer sensitive information from SMs data in an online fashion.

Privacy Preserving Time Series Analysis

Cannot find the paper you are looking for? You can Submit a new open access paper.