Towards Private Deep Learning-Based Side-Channel Analysis Using Homomorphic Encryption

This work investigates using Homomorphic Encryption (HE) to assist the security evaluation of cryptographic devices without revealing side-channel information. For the first time, we evaluate the feasibility of execution of deep learning-based side-channel analysis on standard server equipment using an adapted HE protocol. By examining accuracy and execution time, it demonstrates the successful application of private SCA on both unprotected and protected cryptographic implementations. This contribution is a first step towards confidential side-channel analysis. Our study is limited to the honest-but-curious trust model, where we could reconstruct the secret of an unprotected AES implementation in seconds and of a masked AES implementation in under 17 min.