Paper 2024/967

Consolidated Linear Masking (CLM): Generalized Randomized Isomorphic Representations, Powerful Degrees of Freedom and Low(er)-cost

Abstract

Masking is a widely adopted countermeasure against side-channel analysis (SCA) that protects cryptographic implementations from information leakage. However, current masking schemes often incur significant overhead in terms of electronic cost. RAMBAM, a recently proposed masking technique that fits elegantly with the AES algorithm, offers ultra-low latency/area by utilizing redundant representations of finite field elements. This paper presents a comprehensive generalization of RAMBAM and various other masking schemes within a unified framework and a mathematical representation known as Consolidated Linear Masking (CLM), where masking schemes are formalized by their encoding. We establish a theoretical foundation for CLM linking randomized isomorphic (code) representations and the entropy provided by the redundancy to a revised notion of masking order. Our analysis reveals that RAMBAM is a specific instance of CLM as well as other masking constructions, thus paving the way for significant enhancements. For example, a $$-order secure design can be achieved almost without increasing the size of the representation of the variables. This property scales up to any order and is versatile. We demonstrate how CLM enables: (1) randomized selection of the isomorphic field for improved security; (2) flexible choice of the randomization polynomial; (3) embedded mask-refreshing via the randomized isomorphic representation that reduces randomness requirements significantly as well as improves performance; (4) a wider range of isomorphic randomized mappings that significantly increases the available randomization space compared to RAMBAM; (5) considerable improvement in securing fault-injection attacks and inherent security against probing adversaries, i.e., more required probes. In addition, our framework addresses ways to improve the brute-force parameter choices in the original RAMBAM. By offering a unifying theoretical perspective for masking and practical enhancements, this work advances the design of efficient and secure masking countermeasures against SCA threats.