A Secure and Privacy-Preserving Navigation Scheme Using Spatial Crowdsourcing in Fog-Based VANETs.

Area of Science:

• Computer Science
• Network Engineering
• Cybersecurity

Background:

• Vehicular ad hoc networks (VANETs) are evolving with fog computing integration.
• Real-time navigation in VANETs requires efficient and secure solutions.
• Existing schemes may lack robust privacy preservation and efficient crowdsourcing mechanisms.

Purpose of the Study:

• To propose a secure and privacy-preserving navigation scheme for fog-based VANETs.
• To utilize vehicular spatial crowdsourcing for optimal route discovery.
• To ensure authentication, confidentiality, and conditional privacy.

Main Methods:

• Implementing fog nodes for crowdsourcing task generation and route optimization.
• Utilizing real-time traffic data collected by vehicles.
• Employing cryptographic primitives: Elgamal encryption, AES, randomized anonymous credentials, and group signatures.
• Sequential retrieval of navigation results from fog nodes.

Main Results:

• The proposed scheme effectively generates and releases crowdsourcing tasks via fog nodes.
• Optimal routes are cooperatively determined using real-time vehicle data.
• Vehicles receive rewards for performing crowdsourcing tasks.
• The scheme successfully achieves authentication, confidentiality, and conditional privacy.

Conclusions:

• The developed scheme offers a secure and privacy-preserving navigation solution for fog-based VANETs.
• The integration of vehicular spatial crowdsourcing enhances navigation efficiency.
• The use of advanced cryptographic techniques ensures data security and user privacy.