Mutual information analysis: higher-order statistical moments, efficiency and efficacy

The wide attention given to the mutual information analysis (MIA) is often connected to its statistical genericity , denoted flexibility in this paper. Indeed, MIA is expected to lead to successful key recoveries with no reliance on a priori knowledge about the implementation (impacted by the error modeling made by the attacker. and with as minimum assumptions as possible about the leakage distribution, i.e. able to exploit information lying in any statistical moment and to detect all types of functional dependencies), up to the error modeling which impacts its efficiency (and even its effectiveness). However, emphasis is put on the powerful generality of the concept behind the MIA, as well as on the significance of adequate probability density functions (PDF) estimation which seriously impacts its performance. By contrast to its theoretical advantages, MIA suffers from underperformance in practice limiting its usage. Considering that this underperformance could be explained by suboptimal estimation procedures, we studied in-depth MIA by analyzing the link between the setting of tuning parameters involved in the commonly used nonparametric density estimation, namely kernel density estimation , with respect to three criteria: the statistical moment where the leakage prevails, MIA’s efficiency and its flexibility according to the classical Hamming weight model. The goal of this paper was, therefore, to cast some interesting light on the field of PDF estimation issues in MIA for which much work has been devoted to finding improved estimators having their pros and cons, while little attempt has been made to identify whether existing classical methods can be practically improved or not according to the degree of freedom offered by hyperparameters (when available). We show that some ‘optimal’ estimation procedures following a problem-based approach rather than the systemic use of heuristics following an accuracy-based approach can make MIA more efficient and flexible and a practical guideline for tuning the hyperparameters involved in MIA should be designed. The results of this analysis allowed us defining a guideline based on a detailed comparison of MIA’s results across various simulations and real-world datasets (including publicly available ones such as DPA contest V2 and V4.1).