Reversible secret key encryption of node parameters in neural networks

Abstract

Aspects of the disclosure may comprise a method for encrypting parameters of nodes (e.g. neurons) within an artificial neural network such that private and/or confidential information may be used for training and/or embedded within the artificial neural network with minimal risk of exposure. Aspects of the disclosure further comprise encrypting the node parameters such that the artificial neural network may be trained on the encrypted node parameters and, at runtime, decrypted such that output values reflect the unencrypted node parameters. Aspects of the disclosure may further comprise encrypting the artificial neural network after training has already taken place with unencrypted values to protect private and/or confidential data that may have already been used to train the artificial neural network.