Profiling DPA: Efficacy and Efficiency Trade-Offs

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Abstract

Linear regression-based methods have been proposed as efficient means of characterising device leakage in the training phases of profiled side-channel attacks. Empirical comparisons between these and the ‘classical’ approach to template building have confirmed the reduction in profiling complexity to achieve the same attack-phase success, but have focused on a narrow range of leakage scenarios which are especially favourable to simple (i.e. efficiently estimated) model specifications. In this contribution we evaluate—from a theoretic perspective as much as possible—the performance of linear regression-based templating in a variety of realistic leakage scenarios as the complexity of the model specification varies. We are particularly interested in complexity trade-offs between the number of training samples needed for profiling and the number of attack samples needed for successful DPA: over-simplified models will be cheaper to estimate but DPA using such a degraded model will require more data to recover the key. However, they can still offer substantial improvements over non-profiling strategies relying on the Hamming weight power model, and so represent a meaningful middle-ground between ‘no’ prior information and ‘full’ prior information.