no code implementations • 6 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.
no code implementations • 27 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.
no code implementations • 29 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.
no code implementations • 17 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.
1 code implementation • 12 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.
no code implementations • 20 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.
no code implementations • 29 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.
no code implementations • 10 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.
no code implementations • 11 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.
no code implementations • 10 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.
no code implementations • 14 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.