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IFIP advances in information and communication technology

Quantum-Secure Communication for Trusted Edge Computing with IoT Devices

George Kornaros, Georgia Berki, Miltos D. Grammatikakis

Published Jan 1, 2024• 8 citations
Publisher
DOI
10.1007/978-3-031-56326-3_12

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The rapid advancement of quantum computing poses a long-term threat to the classical cryptographic primitives, such as elliptic-curve and RSA-based key exchange, that currently secure edge-based Internet of Things (IoT) communication. Adversaries capable of harvesting encrypted traffic today may decrypt it once large-scale quantum computers become available, a risk that is especially acute for long-lived edge deployments. This paper presents PQ-SecureEdge, a lightweight post-quantum extension of a previously established zero-trust adaptive cryptographic model for resource-constrained edge computing networks. PQ-SecureEdge introduces two complementary mechanisms: a Lightweight Modular Key Encapsulation (LMKE) scheme built on simplified learning-with-errors modular arithmetic suited to microcontroller-class IoT hardware, and a Trust-Adaptive Session Refresh (TASR) protocol that ties key-rotation frequency to a device's evolving trust score rather than to fixed intervals. Experimental evaluation on a 100-device, five-edge-node testbed shows that PQ-SecureEdge reduces overall key-exchange latency by 21.5% relative to Kyber-512 and 35.4% relative to NTRU-HPS-2048, while lowering memory footprint by 21.3% relative to Kyber-512. Trust-adaptive refresh further lowers average re-keying energy overhead by 41.2% relative to fixed-interval rotation, without measurable loss of security. Security analysis confirms that PQ-SecureEdge preserves a 128-bit post-quantum security margin comparable to NIST-standardized schemes. Statistical analysis across three comparative evaluations confirms the efficiency and robustness of the proposed extension.
Autonomous vehicles (AV) have as one of their characteristics the constant exchange of data with other vehicles, radio base stations, pedestrians and infrastructure. Despite this data exchange using the most modern security techniques, vehicles have been the target of constant cyber-attacks. Emerging technologies such as quantum computing have been tested to minimize these attacks. Particularly, this work investigates the possibility of using post-quantum computing techniques in IoT (Internet of Things) devices, more specifically in AVs. Considering the functionalities of a vehicle nowadays, it is possible to say that a vehicle is composed of a set of IoT devices. Therefore, in this paper, we propose a post-quantum secure method to be used in IoT devices for data exchange between AVs and a 5G base station. In this method, the post-quantum algorithm NTRU is implemented in an edge computing IoT architecture between the source of the CVs data and the edge layer, which is a vulnerable spot for quantum-attacks. We simulated the implementation of the algorithm in a case study and monitored how the algorithm performed. The proposed methodology was capable of providing secure communication between IoT and edge layer. However, the message length and parameters from the algorithm can slow down the communication.
Enhancing data security and privacy in distributed computing environments presents a key challenge in effectively deploying quantum protocols on edge devices with limited resources. The objective of this project is to enhance the security of edge devices by integrating secure quantum communication protocols using Quantum-Enhanced Generative Adversarial Networks (QE-GANs). To provide safe quantum communication integration for QE-GANs on edge devices, researchers can gather the necessary data through the data collection process from edge devices. For data pre-processing in speech recognition applications, Mel-frequency Cepstral Coefficients (MFCCs) are a popular choice. Measurement-Device-Independent Quantum Key Distribution (MDI-QKD) is a cutting-edge method that provides advanced security compared to conventional QKD for secure communication among edge devices. SMLA refers to secure multiparty logic and its utilization, a framework that enables secure communication between multiple parties while ensuring the confidentiality and integrity of the transmitted data. Qubit-masked messages (QMM) and Quantum Coded Modulated Discrete Permutation (QC-MDPC) are two sophisticated quantum communication methods for data encryption. ρ represents the quantum state density matrix; DFR refers to Device-Independent Quantum Secure Randomness Generation Protocol. MATLAB will be utilized to simulate and analyse the performance of QE-GANs) for integrating secure quantum communication protocols into edge devices. The findings show that the peak energy value of 23.7568 represents the dominant frequency component, which plays a key role in significantly influencing the signal. Integrating secure quantum communication protocols with edge devices via QE-GANs could revolutionize data privacy, enhancing real-time encryption, robust authentication, and decentralized trust, paving the way for next-gen secure IoT networks.