A multidimensional, efficient, and secure data query based on privacy preservation in vehicular ad hoc networks

Area of Science:

• Intelligent Transportation Systems
• Network Security
• Data Privacy

Background:

• Vehicular ad hoc networks (VANETs) require efficient and secure data querying for intelligent transportation.
• Existing systems face challenges in multidimensional data processing, privacy leakage, and low computational efficiency in resource-constrained environments.

Purpose of the Study:

• To propose an efficient fine-grained data query system (EFDA) for VANETs.
• To enable secure, real-time querying of multidimensional traffic data while preserving user privacy.

Main Methods:

• Utilizes multidimensional CRT transformation for data vector integration.
• Employs Paillier homomorphic encryption and region feature masking for privacy-preserving aggregation.
• Integrates ECDSA signatures for data integrity and source dependability.
• Implements malicious node monitoring and a reputation incentive mechanism.

Main Results:

• EFDA meets security requirements for data confidentiality, integrity, source reliability, and identity privacy.
• Demonstrates significant reductions in computation overhead (85.7%, 90.1%) and communication overhead (69.1%, 39.2%) compared to a reference scheme.
• Achieves a balance between privacy protection and query efficiency.

Conclusions:

• EFDA is a viable and efficient solution for data querying in resource-constrained VANET environments.
• The system effectively addresses privacy concerns and enhances query performance.