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Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference. (arXiv:2305.02225v1 [cs.CR])
cs.CR updates on arXiv.org arxiv.org
The use of Neural Networks (NNs) for sensitive data processing is becoming
increasingly popular, raising concerns about data privacy and security.
Homomorphic Encryption (HE) has the potential to be used as a solution to
preserve data privacy in NN. This study provides a comprehensive analysis on
the use of HE for NN training and classification, focusing on the techniques
and strategies used to enhance data privacy and security. The current
state-of-the-art in HE for NNs is analysed, and the challenges …
analysis data data privacy data processing encryption homomorphic encryption networks neural networks popular privacy privacy and security security sensitive data solution study training