Zobrazeno 1 - 3
of 3
pro vyhledávání: '"Sébert, Arnaud Grivet"'
While machine learning has become pervasive in as diversified fields as industry, healthcare, social networks, privacy concerns regarding the training data have gained a critical importance. In settings where several parties wish to collaboratively t
Externí odkaz:
http://arxiv.org/abs/2304.02959
This paper tackles the problem of ensuring training data privacy in a federated learning context. Relying on Homomorphic Encryption (HE) and Differential Privacy (DP), we propose a framework addressing threats on the privacy of the training data. Not
Externí odkaz:
http://arxiv.org/abs/2205.04330
We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art of private deep learning against a
Externí odkaz:
http://arxiv.org/abs/2006.09475