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

Jiheon Woo, Donggyun Ryu, Daewon Seo, Young-Sik Kim, Namyoon Lee, Yuval Cassuto, Yongjune Kim

TL;DR

This paper treats side-channel analysis as a Gaussian-additive channel and targets leakage reduction by injecting optimally allocated artificial noise under a fixed power budget. It frames two convex optimization problems: (i) minimizing the total mutual information $I(U;Y^m)$ across leakage points and (ii) minimizing the maximum per-leakage $I(X_i;Y_i)$, yielding dual water-filling-like solutions. The authors extend the framework to Sibson mutual information with a tunable order and demonstrate substantial leakage reductions compared to uniform noise allocations, especially for worst-case leakage. The results suggest that efficient, noise-aware countermeasures can significantly improve SCA resistance for IoT and other resource-constrained systems without prohibitive power costs.

Abstract

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 to minimize the mutual information in systems with Gaussian inputs. Specifically, we formulate two convex optimization problems: 1) minimizing the total mutual information, and 2) minimizing the maximum mutual information across observations. 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.

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

TL;DR

This paper treats side-channel analysis as a Gaussian-additive channel and targets leakage reduction by injecting optimally allocated artificial noise under a fixed power budget. It frames two convex optimization problems: (i) minimizing the total mutual information across leakage points and (ii) minimizing the maximum per-leakage , yielding dual water-filling-like solutions. The authors extend the framework to Sibson mutual information with a tunable order and demonstrate substantial leakage reductions compared to uniform noise allocations, especially for worst-case leakage. The results suggest that efficient, noise-aware countermeasures can significantly improve SCA resistance for IoT and other resource-constrained systems without prohibitive power costs.

Abstract

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 to minimize the mutual information in systems with Gaussian inputs. Specifically, we formulate two convex optimization problems: 1) minimizing the total mutual information, and 2) minimizing the maximum mutual information across observations. 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.
Paper Structure (13 sections, 4 theorems, 27 equations, 3 figures)

This paper contains 13 sections, 4 theorems, 27 equations, 3 figures.

Table of Contents

  1. Introduction
  2. System Model and Backgrounds
  3. Side-Channel Model
  4. Information Leakage Metrics for SCA
  5. Information leakage metrics for SCA
  6. Mutual information
  7. Maximal leakage
  8. Sibson mutual information
  9. Optimal Artificial Noise for Side Channel Analysis Resistance
  10. Minimizing Total Mutual Information
  11. Minimizing Maximum Mutual Information
  12. Numerical Results
  13. conclusion

Key Result

Lemma 1

The optimization problem in eq:capacity_opt is convex with respect to $\{\mathsf{N}_i\}_{i=1}^m$.

Figures (3)

  • Figure 1: Comparison of average (total) mutual information under optimal artificial noise allocation and uniform artificial noise allocation. (a) represents a low SNR scenario with $\mathsf{P}_i\sim\mathcal{N}(1,0.5^2),~\mathsf{Z}_i=1000$. (b) represents a high SNR scenario with $\mathsf{P}_i\sim\mathcal{N}(1,0.5^2),~\mathsf{Z}_i=100$.
  • Figure 2: Comparison of average (total) mutual information under optimal artificial noise allocation and uniform artificial noise allocation (a) represents a low SNR scenario with $\mathsf{P}_i\sim \mathcal{U}[0,2],~\mathsf{Z}_i=1000$. (b) represents a high SNR scenario with $\mathsf{P}_i\sim\mathcal{U}[0,2],~\mathsf{Z}_i=100$.
  • Figure 3: Comparison of the maximum mutual information under optimal artificial noise allocation and uniform artificial noise allocation (a) represents $\mathsf{P}_i\sim\mathcal{N}(1,0.5^2),~\mathsf{Z}_i=1000$. (b) represents $\mathsf{P}_i\sim \mathcal{U}[0,2],~\mathsf{Z}_i=1000$.

Theorems & Definitions (5)

  • Lemma 1
  • Theorem 1
  • Corollary 1
  • Remark 1
  • Theorem 2