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.

翻译：侧信道攻击（SCAs）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。在现有防护措施中，人工噪声注入被认为是最有效的技术之一。然而，其高功耗对物联网（IoT）设备等资源受限系统构成重大挑战，这推动了更高效防护方案的开发。本文通过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，同时约束人工噪声的功耗。我们提出了一种在人工噪声功率约束下最小化互信息的最优人工噪声注入方法。具体而言，我们构建了两个凸优化问题：1）最小化总互信息；2）最小化观测间的最大互信息。我们的第一个主要贡献是针对高斯输入情形提出了最优人工噪声注入框架，此时互信息退化为信道容量——这是量化信息泄漏的一种方式。第二个主要贡献将优化框架扩展至任意输入分布。我们确定了保证优化问题凸性的条件，并利用互信息与最小均方误差之间的基本关系推导出最优解。仿真结果表明，与传统技术相比，所提方法能显著降低总互信息和最大互信息，证实了其在资源受限且安全关键系统中的有效性。