Document Type
Article
Keywords
BB84, FTA, Masking, Pseudonym, Post Quantum Signatures, Sybil
Abstract
Securing big data in power plants is an important and fundamental step in the infrastructure of smart cities. In addition, it becomes a barrier if it is not controlled from the beginning. Security must be a combination of fast and robust properties. This research presents a traffic security system (TSS) in a smart city (SC). It is a novel paradigm meant to improve data security and integrity by means of a multilayered method. Advanced fault analysis, dual-stage pseudonymizing, quantum key distribution via the BB84 protocol, and Falcon signatures (FS) are combined in the proposed system. TSS enhances data security by enhancing privacy, supporting resilience against quantum computing threats, and not burdening the network with complex and large keys. The proposed system has been tested against several recently known attacks, such as replay, supply chain, Sybil, blackhole, eavesdropping, advanced persistent threat (APT), tampering, ransomware identity fraud, and desynchronization, and it has been proven to overcome them. In terms of performance evaluation, the average signing time was approximately 0.002 milliseconds. In comparison, the average signature verification time was approximately 0.004 milliseconds, with an average execution time of approximately 0.54 milliseconds and a precision of approximately 93.6 %, a recall of approximately 97.8 %, and an F1_score of approximately 95.6 %, which are considered low compared with those of state-of-the-art research. Thus, the proposed TSS system is highly acceptable for power plant applications. This work lays the groundwork for future developments in safe, privacy-conscious urban systems by presenting a multilayered approach to secure energy data in smart cities.
How to Cite This Article
Tregi, Tuqa Ghani and Al-Zubaidie, Mishall
(2025)
"Enhancing Traffic Data Security in Smart Cities Using Optimized Quantum-Based Digital Signatures and Privacy-Preserving Techniques,"
Mesopotamian Journal of CyberSecurity: Vol. 5:
Iss.
1, Article 17.
DOI: https://doi.org/10.58496/MJCS/2025/017
Available at:
https://map.researchcommons.org/mjcs/vol5/iss1/17