For a fixed artificial-noise power budget, the optimal allocation that minimizes side-channel information leakage follows a dual water-filling rule for Gaussian leakage points and an I-MMSE-based rule for arbitrary input distributions.
A Case for Maximal Leakage as a Side Channel Leakage Metric
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abstract
Side channels represent a broad class of security vulnerabilities that have been demonstrated to exist in many applications. Because completely eliminating side channels often leads to prohibitively high overhead, there is a need for a principled trade-off between cost and leakage. In this paper, we make a case for the use of maximal leakage to analyze such trade-offs. Maximal leakage is an operationally interpretable leakage metric designed for side channels. We present the most useful theoretical properties of maximal leakage from previous work and demonstrate empirically that conventional metrics such as mutual information and channel capacity underestimate the threat posed by side channels whereas maximal leakage does not. We also study the cost-leakage trade-off as an optimization problem using maximal leakage. We demonstrate that not only can this problem be represented as a linear program, but also that optimal protection can be achieved using a combination of at most two deterministic schemes.
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Mutual Information Minimization for Side-Channel Attack Resistance via Optimal Noise Injection
For a fixed artificial-noise power budget, the optimal allocation that minimizes side-channel information leakage follows a dual water-filling rule for Gaussian leakage points and an I-MMSE-based rule for arbitrary input distributions.