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The use of Neural Networks (NNs) for sensitive data processing is becomingincreasingly popular, raising concerns about data privacy and security.Homomorphic Encryption (HE) has the potential to be used as a solution topreserve data privacy in NN. This study provides a comprehensive analysis onthe use of HE for NN training and classification, focusing on the techniquesand strategies used to enhance data privacy and security. The currentstate-of-the-art in HE for NNs is analysed, and the challenges and limitationsthat need to be addressed to make it a reliable and efficient approach forprivacy preservation are identified. Also, the different categories of HEschemes and their suitability for NNs are discussed, as well as the techniquesused to optimize the accuracy and efficiency of encrypted models. The reviewreveals that HE has the potential to provide strong data privacy guarantees forNNs, but several challenges need to be addressed, such as limited support foradvanced NN operations, scalability issues, and performance trade-offs.

Data Privacy with Homomorphic Encryption in Neural Networks Training and Inference