Achieve Location Privacy-Preserving Range Query in Vehicular Sensing

Area of Science:

• Computer Science
• Information Security
• Networking

Background:

• Vehicles generate vast amounts of local data via on-board sensors and storage.
• Challenges in vehicular sensing include maintaining location privacy and ensuring accurate data queries.
• Existing solutions often struggle to balance privacy with data retrieval efficiency.

Purpose of the Study:

• To propose a novel range query scheme for vehicular ad hoc networks (VANETs).
• To enable accurate retrieval of sensed data from distributed on-board storage while preserving location privacy.
• To address critical issues of location privacy and data query accuracy in vehicular sensing.

Main Methods:

• Utilizes structured scalars to represent locations of data requesters and vehicles.
• Employs the homomorphic Paillier cryptosystem for privacy-preserving location matching.
• Conducts detailed security analysis and performance evaluations.

Main Results:

• The proposed scheme successfully preserves the location privacy of requesters and vehicles.
• Ensures the confidentiality of sensed data.
• Demonstrates efficiency in terms of computation delay and communication overhead, independent of scalar length and proportional only to the number of vehicles.

Conclusions:

• The novel range query scheme effectively balances location privacy and data query accuracy in VANETs.
• The homomorphic Paillier cryptosystem is a viable technique for privacy-preserving location matching.
• The scheme offers an efficient solution for data retrieval in vehicular sensing environments.