Zobrazeno 1 - 10
of 45
pro vyhledávání: '"Messina, Francisco"'
Generative Adversarial Networks (GANs) have been widely used for generating synthetic data for cases where there is a limited size real-world dataset or when data holders are unwilling to share their data samples. Recent works showed that GANs, due t
Externí odkaz:
http://arxiv.org/abs/2311.03172
Detection of adversarial examples has been a hot topic in the last years due to its importance for safely deploying machine learning algorithms in critical applications. However, the detection methods are generally validated by assuming a single impl
Externí odkaz:
http://arxiv.org/abs/2206.15415
Location of non-stationary forced oscillation (FO) sources can be a challenging task, especially in cases under resonance condition with natural system modes, where the magnitudes of the oscillations could be greater in places far from the source. Th
Externí odkaz:
http://arxiv.org/abs/2108.08736
Publikováno v:
2021 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2021, pp. 333-338
Fine-grained Smart Meters (SMs) data recording and communication has enabled several features of Smart Grids (SGs) such as power quality monitoring, load forecasting, fault detection, and so on. In addition, it has benefited the users by giving them
Externí odkaz:
http://arxiv.org/abs/2107.08315
Autor:
Picot, Marine, Messina, Francisco, Boudiaf, Malik, Labeau, Fabrice, Ayed, Ismail Ben, Piantanida, Pablo
Adversarial robustness has become a topic of growing interest in machine learning since it was observed that neural networks tend to be brittle. We propose an information-geometric formulation of adversarial defense and introduce FIRE, a new Fisher-R
Externí odkaz:
http://arxiv.org/abs/2106.06685
Publikováno v:
2019 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2019, pp. 1-7
The explosion of data collection has raised serious privacy concerns in users due to the possibility that sharing data may also reveal sensitive information. The main goal of a privacy-preserving mechanism is to prevent a malicious third party from i
Externí odkaz:
http://arxiv.org/abs/2011.11421
Publikováno v:
2020 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2020, pp. 1-6
Smart meters (SMs) can pose privacy threats for consumers, an issue that has received significant attention in recent years. This paper studies the impact of Side Information (SI) on the performance of distortion-based real-time privacy-preserving al
Externí odkaz:
http://arxiv.org/abs/2006.16062
The rapid development and expansion of the Internet of Things (IoT) paradigm has drastically increased the collection and exchange of data between sensors and systems, a phenomenon that raises serious privacy concerns in some domains. In particular,
Externí odkaz:
http://arxiv.org/abs/2006.06106
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. However, this could leak sensitive information about the consumers to the UP or a th
Externí odkaz:
http://arxiv.org/abs/2003.04946
Publikováno v:
in IEEE Transactions on Smart Grid, vol. 11, no. 6, pp. 5174-5183, Nov. 2020
Smart Meters (SMs) are able to share the power consumption of users with utility providers almost in real-time. These fine-grained signals carry sensitive information about users, which has raised serious concerns from the privacy viewpoint. In this
Externí odkaz:
http://arxiv.org/abs/1906.06427