A privacy-preserving multi-party data aggregation scheme for the internet of vehicles

Data aggregation can integrate scattered and isolated vehicle data into useful information for real-time traffic analysis, road condition prediction, or intelligent decision-making. In the Internet of Vehicles (IoV) scenario, secure data aggregation ensures the privacy and security of original vehicle data (such as location, speed, and driving status), which is of great significance for complying with user privacy protection regulations and preventing sensitive information leakage. Based on the threshold NTRU encryption algorithm and a novel encoding method, this paper proposes a multi-party secure data aggregation protocol suitable for IoV. Furthermore, to address potential cheating behaviors by malicious participants, an anti-cheating protocol is designed to detect and prevent the submission of false data in critical steps. Both protocols can compute statistical metrics (total count, sum, average, maximum, minimum, median, variance, and standard deviation) for multi-party vehicle data in a single run and support partial result output on demand. Simulation-based proofs demonstrate that the protocols can resist $$n-1$$ collusion attacks. Performance analysis and experimental tests show that the protocols outperform existing schemes in computational and communication efficiency, and are particularly adaptable to the high dynamics and real-time requirements of IoV. This study provides an efficient and feasible solution to the problem of privacy-preserving data aggregation in IoV.