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

2025-04-29

AI Summary

IoT devices are highly vulnerable to side-channel attacks (SCAs), yet conventional hardware-level countermeasures—such as manually designed artificial noise—suffer from excessive power overhead, rendering them impractical for resource-constrained deployments. Method: This work models the side-channel leakage as a power-constrained communication system and, for the first time, rigorously formulates minimizing the mutual information between secret keys and physical observations as a convex optimization problem. We propose a bi-objective framework jointly optimizing both total mutual information and maximum pointwise mutual information, overcoming the limitations of heuristic noise design. Leveraging information-theoretic modeling, semidefinite programming (SDP), and Gaussian channel analysis, we co-design the noise distribution and leakage model. Results: Experiments demonstrate that, under identical noise power budgets, our approach reduces total mutual information by 47% and maximum pointwise mutual information by over 63%, significantly outperforming uniform and Gaussian noise baselines. The method establishes a provably secure, energy-efficient SCA mitigation paradigm tailored for ultra-low-power IoT scenarios.

Technology Category

• Machine Learning: Hardware-aware ML
• Search and Optimization: Mixed Discrete/Continuous Search
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Security and Privacy: Large-scale security measurements
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems

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.

Problem

• Minimizing mutual information to resist side-channel attacks
• Optimizing noise injection under power constraints
• Reducing leakage in resource-constrained systems like IoT

Innovation

• Optimal noise injection minimizes mutual information
• Convex optimization for total and maximum leakage
• Gaussian input systems with power constraints