A Lightweight Privacy-Preserving Scheme for Verifiable Multidimensional Data Aggregation in Vehicular Crowdsensing Networks

In the context of vehicular crowdsensing within the Internet of Vehicles (IoV), data aggregation techniques enable the computation and analysis of sensing data to extract valuable insights and improve transmission efficiency. However, sensing data and aggregation results often contain sensitive information about terminal vehicles, posing risks of privacy leakage. Existing privacy-preserving data aggregation schemes typically employ homomorphic encryption or bilinear pairing operations to ensure both privacy protection and integrity verification, which incur significant computational overhead. Moreover, when aggregation nodes are untrusted, it becomes challenging to verify the correctness of the aggregated results. To address these challenges, this paper proposes a lightweight and verifiable multi-dimensional data aggregation privacy-preserving scheme for vehicular crowdsensing. In the data generation phase, a blinding factor is introduced to obfuscate the sensing data, and secret sharing is employed to split the obfuscated data into multiple shares. This approach ensures data privacy and resists collusion attacks among internal vehicles. A lightweight signature aggregation method is integrated to verify multiple digital signatures in a single computation, significantly reducing the computational and communication costs associated with integrity verification. In the data recovery phase, the original data can be restored by removing the blinding factor, eliminating the need for traditional decryption algorithms and thereby reducing computational overhead. In the aggregation result verification phase, a homomorphic commitment mechanism is adopted. Users generate commitments of the obfuscated sensing data and upload them to the blockchain, effectively addressing the issue of untrusted roadside units and traffic management centers in real-world applications and ensuring the correctness of the aggregated results. Experimental results demonstrate that the proposed scheme reduces computational overhead by more than 50% compared to existing methods, exhibiting higher efficiency and better adaptability.