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Optimizing Noise Injection to Block Side-Channel Attacks: An Information-Theoretic Approach for Low-Power Devices In Plain English: Some hackers can steal secret codes from devices by watching how much power they use or how long they take to do tasks. This paper tackles that problem by adding just the right kind of electronic 'noise' to hide those clues. The researchers figured out the smartest way to add this noise so it uses as little extra power as possible, which is important for small devices like smart sensors. They tested their method and found it works better than older techniques at keeping secrets safe without draining the battery. This means we can make small, secure devices that are harder to hack using physical tricks. Summary: Side-channel attacks (SCAs) exploit physical leakages—such as power consumption, timing, and electromagnetic emissions—to extract secret cryptographic keys from devices, posing a major threat to system security, especially in resource-constrained environments like IoT devices (Woo et al., 2026). While artificial noise injection is a known countermeasure, its high power consumption limits its practicality. This paper proposes a novel method that models the side-channel as a communication channel and aims to minimize the mutual information between secret data and observable side-channel traces, subject to a power constraint on the injected noise. The authors focus on systems with Gaussian inputs and formulate two convex optimization problems: (1) minimizing the total mutual information across all observations, and (2) minimizing the maximum mutual information in any single observation to prevent worst-case leakage. By solving these optimization problems, the method determines the optimal noise distribution that maximizes security while minimizing power overhead. Numerical evaluations demonstrate that the proposed approach significantly reduces both total and peak mutual information compared to conventional noise injection techniques, confirming its effectiveness. This work provides a theoretically rigorous and practically efficient solution for enhancing side-channel attack resistance in low-power, security-critical systems. Key Points: - Side-channel attacks (SCAs) exploit physical leakages to extract secret keys and are a major threat to device security. - Artificial noise injection is effective but often too power-hungry for IoT and embedded systems. - This paper models SCAs using information theory, treating leakage as a communication channel. - The goal is to minimize mutual information between secret data and side-channel observations under a power budget. - Two convex optimization problems are proposed: minimizing total mutual information and minimizing maximum mutual information. - The optimal noise injection strategy is derived for Gaussian input systems. - Numerical results show significant reduction in information leakage compared to baseline methods. - The method is especially suited for resource-constrained, security-critical applications like IoT devices. Notable Quotes: - "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." (Woo et al., 2026) - "Numerical 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." (Woo et al., 2026) Data Points: - Paper version history: First submitted on 29 Apr 2025, last updated on 23 Jan 2026. - Multiple revisions show iterative improvements, with file sizes ranging from 665 KB to 1,934 KB. - The proposed method achieves significant reduction in both total and maximum mutual information in numerical simulations (exact percentages not specified in abstract, but implied as a key result). Controversial Claims: - The assumption that side-channel leakage can be accurately modeled as a Gaussian communication channel may not hold for all physical implementations, potentially limiting the method’s generalizability. - The claim that mutual information minimization fully captures practical attack resistance could be debated, as real-world side-channel attacks often rely on higher-order statistical properties or machine learning models that may not be fully mitigated by first-order information-theoretic suppression. Technical Terms: - Side-channel attacks (SCAs), mutual information, artificial noise injection, Gaussian inputs, convex optimization, information leakage, communication channel model, power constraint, optimization objective, total mutual information, maximum mutual information, resource-constrained systems, IoT security, information-theoretic security, side-channel observations —Ada H. Pemberley Dispatch from The Prepared E0