Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Sengupta, Poushali"'
Autor:
Duguma, Daniel Gerbi, Zhang, Juliana, Aboutalebi, Meysam, Zhang, Shiliang, Banet, Catherine, Bjørkli, Cato, Baramashetru, Chinmayi, Eliassen, Frank, Zhang, Hui, Muringani, Jonathan, Noll, Josef, Fostervold, Knut Inge, Böcker, Lars, Bygrave, Lee Andrew, Bagherpour, Matin, Moghadam, Maunya Doroudi, Owe, Olaf, Sengupta, Poushali, Vitenberg, Roman, Maharjan, Sabita, Garrett, Thiago, Li, Yushuai, Shan, Zhengyu
This manuscript aims to formalize and conclude the discussions initiated during the PriTEM workshop 22-23 March 2023. We present important ideas and discussion topics in the context of transactive energy systems. Moreover, the conclusions from the di
Externí odkaz:
http://arxiv.org/abs/2312.11564
Explainability of AI models is an important topic that can have a significant impact in all domains and applications from autonomous driving to healthcare. The existing approaches to explainable AI (XAI) are mainly limited to simple machine learning
Externí odkaz:
http://arxiv.org/abs/2305.14098
Autor:
Sengupta, Poushali, Mishra, Subhankar
Privacy and Fairness both are very important nowadays. For most of the cases in the online service providing system, users have to share their personal information with the organizations. In return, the clients not only demand a high privacy guarante
Externí odkaz:
http://arxiv.org/abs/2105.07244
Federated learning (FL) is a distributed learning process where the model (weights and checkpoints) is transferred to the devices that posses data rather than the classical way of transferring and aggregating the data centrally. In this way, sensitiv
Externí odkaz:
http://arxiv.org/abs/2009.06005
The leakage of data might have been an extreme effect on the personal level if it contains sensitive information. Common prevention methods like encryption-decryption, endpoint protection, intrusion detection system are prone to leakage. Differential
Externí odkaz:
http://arxiv.org/abs/2006.05609
Balancing utility and differential privacy by shuffling or \textit{BUDS} is an approach towards crowd-sourced, statistical databases, with strong privacy and utility balance using differential privacy theory. Here, a novel algorithm is proposed using
Externí odkaz:
http://arxiv.org/abs/2006.04125
Publikováno v:
Digital Threats: Research & Practice; Jun2023, Vol. 4 Issue 2, p1-23, 23p
