Efficient Encryption Schemes for Neural Networks

Abstract

With the increasing adoption of neural networks in various applications, ensuring the security of these models becomes paramount. This paper explores efficient encryption schemes tailored for neural networks, addressing the challenges of protecting model integrity and privacy during both training and deployment phases. We propose novel methods that leverage cryptographic techniques to encrypt neural network weights and activations while maintaining computational efficiency. Our approach aims to mitigate the risks associated with model inversion attacks and unauthorized access to sensitive data. Through experimental validation, we demonstrate the effectiveness of our encryption schemes in maintaining model accuracy and performance while enhancing security. This work contributes to the development of robust security measures for neural networks, enabling their safe deployment in sensitive environments.