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.

翻译：侧信道攻击（SCA）通过功耗、时序变化和电磁辐射等物理泄漏提取密钥，对系统安全构成严重威胁。在现有防御措施中，人工噪声注入被公认为最有效的技术之一，但其高功耗对物联网（IoT）设备等资源受限系统构成重大挑战，亟需开发更高效的防护方案。本文将侧信道攻击建模为通信信道，旨在通过最小化秘密信息与侧信道观测之间的互信息来抑制信息泄漏，同时对人工噪声施加功率约束。首先考虑高斯输入情形，此时互信息转化为信道容量，可作为量化信息泄漏的指标之一。进而将该框架扩展至任意输入分布，通过识别优化问题保持凸性的条件，并利用基本的I-MMSE关系推导最优噪声分配。数值结果表明，与传统方法相比，所提方法能够显著降低互信息，证明了其在严格功率约束下安全关键系统中的有效性。