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 propose an optimal artificial noise injection method that minimizes the mutual information under power constraints for artificial noise. Specifically, we formulate two convex optimization problems: 1) minimizing the total mutual information, and 2) minimizing the maximum mutual information across observations. Our first major contribution is proposing an optimal artificial noise injection framework for the case of Gaussian input, where the mutual information becomes the channel capacity, which is one way to quantify the information leakage. Our second major contribution extends the optimization framework to arbitrary input distributions. We identify conditions ensuring the convexity of the optimization problem and derive the optimal solution using the fundamental relationship between the mutual information and the minimum mean squared error. The simulation results show that the proposed methods significantly reduce both total and maximum mutual information compared to conventional techniques, confirming their effectiveness for resource-constrained, security-critical systems.