Optimizing Higher-Order Correlation Analysis Against Inner Product Masking Scheme
Jingdian Ming, Yongbin Zhou, Wei Cheng, Huizhong Li · IEEE Transactions on Information Forensics and Security · 2022
In recent years, inner product masking (IPM) has been proposed as a promising code based masking scheme against side-channel attacks. However, most studies mainly focus on leakages in terms of information-theoretic metrics, and simulated experiments utilizing profiled attacks (with known templates). In this paper, we investigate the security of IPM scheme against one typical non-profiled side-channel attack, namely higher-order correlation analysis. We demonstrate that three factors mainly influence the efficiency of higher-order correlation analysis against IPM, and they are the attack order vector, output size of the target function and attack model. In particular, we propose a new method to measure the efficiency of higher-order correlation analyses. Generally speaking, this method is based on the correlation coefficient between leakage expectations with certain shares and sensitive values under the attack model. We validate our method by both simulation-based experiments and practical one with different attack factors. All experiments are running on the IPM in the different noise scenarios. The results demonstrate that our method works well as an indicator for higher-order correlation analyses against IPM.