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

Abstract

For vehicular ad hoc networks (VANET) to achieve intelligent transportation applications, efficient and secure data querying is essential. However, sophisticated multidimensional data processing, easy user privacy leaks, and low computational efficiency in resource-constrained contexts are some of the main issues that data querying in VANET environments encounters. To address these issues, this paper proposes an efficient fine-grained data query system (EFDA) based on lightweight masks that allows vehicle users to safely and in real-time query multidimensional traffic data. First, multifaceted data vectors are effectively integrated into a single cipher processing unit using a multidimensional CRT transformation method that counts the number of valid data. Paillier homomorphic encryption and the lightweight region feature masking technique are used to provide safe aggregation while preserving the privacy of the original data. Second, the ECDSA signature is used to ensure source dependability and data integrity. Lastly, to lower system risk and enhance data quality, an effective malicious node monitoring method based on dichotomous recursion and a reputation incentive mechanism based on user feedback is presented. According to security analysis, the EFDA scheme meets the threat model’s specified security requirements for data confidentiality, integrity, source reliability, and identity privacy. According to the performance simulation evaluation, the EFDA system lowers the computation overhead by 85.7% and 90.1% and the communication overhead by 69.1% and 39.2% when compared to the reference scheme. It achieves the balance between privacy protection and query efficiency and validates its viability and efficiency in the resource-constrained in-vehicle network environment.