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When approximate design for fast homomorphic computation provides differential privacy guarantees
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.
Protecting Data from all Parties: Combining FHE and DP in Federated Learning
This paper tackles the problem of ensuring training data privacy in a federated learning context.
SPEED: Secure, PrivatE, and Efficient Deep learning
Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art of private deep learning against a wider range of threats, in particular the honest-but-curious server assumption.
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