Efficient Profiled Attacks on Masking Schemes

Abstract

Side-channel adversaries represent real-world threats against (certified and uncertified) cryptographic devices. Masking schemes represent prevailing countermeasures to reduce the success probabilities of side-channel attacks. However, masking schemes increase the implementation cost in terms of power consumption, clock cycles, and random number generation. Investigation of tools evaluating the degree of resilience of cryptographic devices using masking (against side-channel attacks) represents an important aspect in certification procedures (e.g., common criteria, FIPS 140-2, and EMVco). Several side-channel evaluation techniques exist such as template attacks and machine learning-based attacks. In this paper, we formalize results obtained in side-channel attacks community when targeting masked implementations. We report theoretical as well as practical results of parametric and non-parametric side-channel attacks on masking schemes. The theoretical part reports results based on a simulation of the execution of software devices, while the practical part focuses on actual leakages measured during the execution of a software implementation in three different contexts (that contain different levels of noise).