Incentive Mechanism for Privacy-Preserving Collaborative Routing Using Secure Multi-Party Computation and Blockchain

Area of Science:

• Computer Science
• Transportation Engineering
• Cybersecurity

Background:

• Traffic congestion is a significant issue stemming from demand-supply imbalances.
• Connected vehicle technologies offer potential solutions through incentive mechanisms for collaborative routing.
• Existing mechanisms face privacy challenges due to data sharing and validation requirements.

Purpose of the Study:

• To propose a novel privacy-preserving incentive mechanism for collaborative routing in Vehicle-to-Everything (V2X) systems.
• To address the privacy concerns associated with information sharing and execution validation in traffic management.
• To enhance the efficiency and security of collaborative routing strategies.

Main Methods:

• Leveraging secure Multi-party Computation (MPC) for off-chain sensitive data processing and incentive computation.
• Utilizing blockchain technology for secure storage of route validation data and incentive management.
• Implementing a route validation scheme with position proofs generated via off-chain threshold signatures with witness vehicles.
• Employing cascading multi-signature wallets to temporarily freeze incentives for security.

Main Results:

• The proposed scheme effectively preserves user privacy during collaborative routing.
• The integration of MPC and blockchain ensures secure incentive distribution and route validation.
• Numerical studies demonstrate the efficiency and implementability of the developed incentive mechanism.
• The system successfully mitigates privacy risks inherent in V2X collaborative routing.

Conclusions:

• The developed privacy-preserving incentive mechanism offers a viable solution for traffic congestion in V2X environments.
• The combination of MPC and blockchain technology enhances security and privacy in collaborative routing.
• The proposed approach is both efficient and practical for real-world implementation.