Exposing Side-Channel Leakage of SEAL Homomorphic Encryption Library

Abstract

This paper reveals a new side-channel leakage of Microsoft SEAL homomorphic encryption library. The proposed attack exploits the leakage of ternary value assignments made during the Number Theoretic Transform (NTT) sub-routine. Notably, the attack can steal the secret key coefficients from a single power/electromagnetic measurement trace. To achieve high accuracy with a single-trace, we build a novel machine-learning based side-channel profiler. Moreover, we implement a defense based on random delay insertion based defense mechanism to mitigate the shown leakage. The results on an ARM Cortex-M4F processor show that our attack extracts secret key coefficients with 98.3% accuracy and random delay insertion defense does not reduce the success rate of our attack.