Randomness Optimization for Gadget Compositions in Higher-Order Masking

Physical characteristics of electronic devices, leaking secret and sensitive information to an adversary with physical access, pose a long-known threat to cryptographic hardware implementations. Among a variety of proposed countermeasures against such Side-Channel Analysis attacks, masking has emerged as a promising, but often costly, candidate. Furthermore, the manual realization of masked implementations has proven error-prone and often introduces flaws, possibly resulting in insecure circuits. In the context of automatic masking, a new line of research emerged, aiming to replace each physical gate with a secure gadget that fulfills well-defined properties, guaranteeing security when interconnected to a large circuit. Unfortunately, those gadgets introduce a significant amount of additional overhead into the design, in terms of area, latency, and randomness requirements. In this work, we present a novel approach to reduce the demands for randomness in such gadget-composed circuits by reusing randomness across gadgets while maintaining security in the probing adversary model. To this end, we embedded the corresponding optimization passes into an Electronic Design Automation toolchain, able to construct, optimize, and implement masked circuits, starting from an unprotected design. As such, our security-aware optimization offers an additional building block for existing or new Electronic Design Automation frameworks, where security is considered a first-class design constraint