Secret-Key Agreement Through Hidden Markov Modeling of Wavelet Scattering Embeddings

2025-10-10

AI Summary

Channel-reciprocity-based key generation in IoT is vulnerable to noise, sampling asynchrony, and channel fading, while conventional quantization introduces irreversible errors. To address these limitations, this paper proposes a quantization-free, end-to-end key agreement framework. It employs wavelet scattering networks to extract robust, reciprocal channel state features; applies dimensionality reduction to uncover latent clustering structures; and models the stochastic evolution of wireless channels via hidden Markov models—thereby eliminating reliance on instantaneous sampling synchronization. Experimental results demonstrate that the proposed scheme achieves a fivefold increase in key generation rate over conventional approaches, significantly enhancing both security and efficiency in resource-constrained IoT environments.

Technology Category

• Machine Learning: Quantum Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

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
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Secret-key generation and agreement based on wireless channel reciprocity offers a promising avenue for securing IoT networks. However, existing approaches predominantly rely on the similarity of instantaneous channel measurement samples between communicating devices. This narrow view of reciprocity is often impractical, as it is highly susceptible to noise, asynchronous sampling, channel fading, and other system-level imperfections -- all of which significantly impair key generation performance. Furthermore, the quantization step common in traditional schemes introduces irreversible errors, further limiting efficiency. In this work, we propose a novel approach for secret-key generation by using wavelet scattering networks to extract robust and reciprocal CSI features. Dimensionality reduction is applied to uncover hidden cluster structures, which are then used to build hidden Markov models for efficient key agreement. Our approach eliminates the need for quantization and effectively captures channel randomness. It achieves a 5x improvement in key generation rate compared to traditional benchmarks, providing a secure and efficient solution for key generation in resource-constrained IoT environments.

Problem

• Overcoming noise and imperfections in wireless channel reciprocity
• Eliminating irreversible errors from quantization in key generation
• Improving key generation rates for resource-constrained IoT networks

Innovation

• Wavelet scattering networks extract robust CSI features
• Hidden Markov models capture channel randomness structures
• Quantization-free approach improves key generation rate