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

Meshari D Alanazi1, Mohammed Albekairi1, Ghulam Abbas2

  • 1Department of Electrical Engineering, College of Engineering, Jouf University, Sakakah 72388, Saudi Arabia.

Sensors (Basel, Switzerland)
|January 11, 2025
PubMed
Summary

This study introduces a resilient privacy-preserving method for intelligent transportation systems (ITS) in smart cities. The novel approach enhances privacy protection by 30% and reduces computational overhead by 25% while ensuring secure data sharing.

Keywords:
forward secrecyintelligent transportationmachine learningsmart citiestransfer learning

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Area of Science:

  • Computer Science
  • Cybersecurity
  • Transportation Engineering

Background:

  • Intelligent Transportation Systems (ITS) in smart cities facilitate movement but face privacy risks due to data sharing.
  • Adversary risks in communication paradigms threaten user privacy, impacting mobility and localization.
  • Existing ITS architectures require enhanced security measures to address privacy vulnerabilities.

Purpose of the Study:

  • To introduce a novel resilient privacy-preserving (RPP) method for secure communication in smart city ITS.
  • To enhance user privacy and system resilience against security threats in traffic data exchange.
  • To reduce computational overhead while improving privacy protection in ITS.

Main Methods:

  • A resilient privacy-preserving (RPP) method utilizing presumed secrecy (PS) is proposed.
  • Privacy is maintained based on previous security depletion levels for progressive communication sessions.
  • Dodged transfer learning is employed to identify interruptions and recommend re-handoffs in traffic data sessions.

Main Results:

  • The RPP method achieved a 25% reduction in computational overhead compared to conventional methods.
  • Privacy protection was enhanced by 30% over existing approaches.
  • Security depletion rates decreased by 15% across various traffic densities, showing improved ITS resilience.

Conclusions:

  • The proposed RPP method offers a robust solution for privacy preservation in smart city ITS.
  • The approach effectively balances security, privacy, and efficiency in intelligent transportation communication.
  • Empirical results validate the model's efficacy in enhancing secure ITS communication and resilience.