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Securing Data in Vehicles: Privacy-Preserving Frameworks for Dynamic CAV Environments.

Rahma Hammedi1, David J Brown1, Omprakash Kaiwartya1

  • 1Department of Computer Science, Nottingham Trent University, Nottingham NG11 8NS, UK.

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Summary
This summary is machine-generated.

Connected and Autonomous Vehicles (CAVs) generate vast data, posing privacy risks. This paper explores privacy challenges and solutions like Federated Learning, blockchain, and SDN for secure intelligent transportation systems.

Keywords:
CAVsfederated learningpermissioned blockchainprivacysoftware defined vehicular networking

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Area of Science:

  • Intelligent Transportation Systems
  • Cybersecurity
  • Data Privacy

Background:

  • Connected and Autonomous Vehicles (CAVs) integrate advanced automation and connectivity.
  • Vehicle-to-Everything (V2X) communication is crucial for CAVs but raises privacy concerns.
  • Dynamic CAV environments present unique challenges for privacy preservation.

Purpose of the Study:

  • Investigate evolving data privacy risks in CAV systems.
  • Critically review existing privacy-preserving approaches and their limitations.
  • Explore enabling technologies for privacy preservation in CAVs.

Main Methods:

  • Literature review of privacy-preserving mechanisms.
  • Analysis of Federated Learning, permissioned blockchain, and Software-Defined Networking (SDN).
  • Assessment of technological limitations in dynamic vehicular contexts.

Main Results:

  • Existing privacy models struggle with the dynamic nature of CAVs.
  • Federated Learning, blockchain, and SDN show promise for privacy preservation.
  • Specific limitations of current approaches in high-mobility environments were identified.

Conclusions:

  • Targeted recommendations for optimizing privacy frameworks in CAVs.
  • Enhancing privacy resilience is key for next-generation intelligent transportation.
  • Need for scalable and robust privacy solutions for widespread CAV adoption.