SoK: A Methodology to Achieve Provable Side-Channel Security in Real-World Implementations
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Abstract
A wide range of countermeasures have been proposed to defend against side-channel attacks, with masking being one of the most effective and commonly used techniques. While theoretical models provide formal security proofs, these often rely on assumptions—sometimes implicit—that can be difficult to assess in practice. As a result, the design of secure masked implementations frequently combines proven theoretical arguments with heuristic and empirical validation.
Despite the significant body of work, the literature still lacks a cohesive and well-defined framework for translating theoretical security guarantees into practical implementations on physical devices. Specifically, there remains a gap in connecting provable results from abstract models to quantitative security guarantees at the implementation level.
In this Systematization of Knowledge (SoK), we aim to provide a comprehensive methodology to transform abstract cryptographic algorithms into physically secure implementations against side-channel attacks on microcontrollers. We introduce new tools to adapt the ideal noisy leakage model to practical, real-world scenarios, and we integrate state-of-the-art techniques to build secure implementations based on this model.
Our work systematizes the design objectives necessary for achieving high security levels in embedded devices and identifies the remaining challenges in concretely applying security reductions. By bridging the gap between theory and practice, we seek to provide a foundation for future research that can develop implementations with proven security against side-channel attacks, based on well-understood leakage assumptions.
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Sonia Belaïd, Gaëtan Cassiers, Camille Mutschler, Matthieu Rivain, Thomas Roche, François-Xavier Standaert, and Abdul Rahman Taleb, SoK: A Methodology to Achieve Provable Side-Channel Security in Real-World Implementations. IACR Communications in Cryptology, vol. 2, no. 1, Apr 08, 2025, doi: 10.62056/aebngy4e-.
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This work is licensed under a Creative Commons Attribution (CC BY) license.