Mutual Information Minimization for Side-Channel Attack Resistance via Optimal Noise Injection

Side-channel attacks (SCAs) pose a serious threat to system security by extracting secret keys through physical leakages such as power consumption, timing variations, and electromagnetic emissions. Among existing countermeasures, artificial noise injection is recognized as one of the most effective techniques. However, its high power consumption poses a major challenge for resource-constrained systems such as Internet of Things (IoT) devices, motivating the development of more efficient protection schemes. In this paper, we model SCAs as a communication channel and aim to suppress information leakage by minimizing the mutual information between the secret information and side-channel observations, subject to a power constraint on the artificial noise. We first consider the Gaussian input case, where the mutual information becomes the channel capacity, which is one way to quantify the information leakage. We then extend the framework to arbitrary input distributions by identifying conditions under which the optimization remains convex and by leveraging the fundamental I-MMSE relationship to derive the optimal noise allocation. Numerical results show that the proposed methods substantially reduce mutual information compared with conventional techniques, demonstrating their effectiveness for security-critical systems operating under tight power constraints.