Information Leakage in Code-Based Masking: A Systematic Evaluation by Higher-Order Attacks

Abstract

Code-based masking is a recent line of research on masking schemes aiming at provably counteracting side-channel attacks. It generalizes and unifies many masking schemes within a coding-theoretic formalization. In code-based masking schemes, the tuning parameters are the underlying linear codes, whose choice significantly affects the side-channel resilience. In this paper, we investigate the exploitability of the information leakage in code-based masking and present attack-based evaluation results of higher-order optimal distinguisher (HOOD). Particularly, we consider two representative instances of code-based masking, namely inner product masking (IPM) and Shamir’s secret sharing (SSS) based masking. Our results do confirm the state-of-the-art theoretical derivatives in an empirical manner with numerically simulated measurements. Specifically, theoretical results are based on quantifying information leakage; we further complete the panorama with attack-based evaluations by investigating the exploitability of the leakage. Moreover, we classify all possible candidates of linear codes in IPM with 2 and 3 shares and (3, 1)-SSS based masking, and highlight both optimal and worst codes for them. Relying on our empirical evaluations, we therefore recommend investigating the coding-theoretic properties to find the best linear codes in strengthening instances of code-based masking. As for applications, our attack-based evaluation directly empowers designers, by employing optimal linear codes, to enhance the protection of code-based masking. Our framework leverages simulated leakage traces, hence allowing for source code validation or patching in case it is found to be attackable.