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SABLE: Secure And Byzantine robust LEarning
SABLE leverages HTS, a novel and efficient homomorphic operator implementing the prominent coordinate-wise trimmed mean robust aggregator.
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.
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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