Profiling DPA: Efficacy and Efficiency Trade-Offs
نویسندگان
چکیده
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 tradeoffs 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 middleground between ‘no’ prior information and ‘full’ prior information.
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تاریخ انتشار 2013