Resilient Privacy Preservation Through a Presumed Secrecy Mechanism for Mobility and Localization in Intelligent Transportation Systems

Research output: Journal contributionsJournal articlesResearchpeer-review

Authors

  • Meshari D. Alanazi
  • Mohammed Albekairi
  • Ghulam Abbas
  • Turki M. Alanazi
  • Khaled Kaaniche
  • Gehan Elsayed
  • Paolo Mercorelli

An intelligent transportation system (ITS) offers commercial and personal movement through the smart city (SC) communication paradigms with hassle-free information sharing. ITS designs and architectures have improved via information and communication technologies in recent years. The information shared through the communication medium in SCs is exposed to adversary risk, resulting in privacy issues. Privacy issues impact the contingent mobility and localization of the ITS path. This paper introduces a novel resilient privacy preserving (RPP) method through presumed secrecy (PS) to provide a robust privacy measure. The privacy of the progressive communication sessions is preserved based on the previous security depletion levels. The interruptions in traffic data-related communication sessions are recurrently identified, and re-handoffs are recommended with dodged transfer learning. The empirical results indicate a 25% reduction in computational overhead and a 30% enhancement in privacy protection over conventional methods, demonstrating the model’s efficacy in secure ITS communication. Compared with existing methods, the proposed approach decreases security depletion rates by 15% across varying traffic densities, underscoring ITS resilience in high-interaction scenarios.

Original languageEnglish
Article number115
JournalSensors
Volume25
Issue number1
Number of pages20
ISSN1424-8239
DOIs
Publication statusPublished - 01.2025

Bibliographical note

Publisher Copyright:
© 2024 by the authors.

    Research areas

  • forward secrecy, intelligent transportation, machine learning, smart cities, transfer learning
  • Engineering

DOI

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