Power side-channel attacks, which can deduce secret data via statistical analysis, have become a serious threat. Masking is an effective countermeasure for reducing the statistical dependence between secret data and side-channel information. However, designing masking algorithms is an error-prone process. In this paper, we propose a hybrid approach combing type inference and model-counting to verify masked arithmetic programs against side-channel attacks. The type inference allows an efficient, lightweight procedure to determine most observable variables whereas model-counting accounts for completeness. In case that the program is not perfectly masked, we also provide a method to quantify the security level of the program. We implement our methods in a tool QMVerif and evaluate it on cryptographic benchmarks. The experimental results show the effectiveness and efficiency of our approach.
@article{arxiv.1901.09706,
title = {Quantitative Verification of Masked Arithmetic Programs against Side-Channel Attacks},
author = {Pengfei Gao and Hongyi Xie and Jun Zhang and Fu Song and Taolue Chen},
journal= {arXiv preprint arXiv:1901.09706},
year = {2019}
}