Privacy-Preserving Group-by-Aggregation Queries for Data Federation under V2X environment
Zicheng Cao, Guanfeng Liu, Qingzhi Ma, Wei Chen, Lei Zhao, An Liu · ACM Transactions on Autonomous and Adaptive Systems · 2025
Vehicle-to-everything (V2X) technology enables vehicles to communicate with each other, infrastructure, and the cloud, facilitating intelligent traffic management and vehicle interconnection. However, the data generated by vehicles raises concerns regarding personal privacy and corporate interests. With the rapid development of V2X technology, data security issues are becoming increasingly prominent. Data federation, as an emerging data-sharing model, utilizes secure multi-party computation techniques to enable collaboration among data owners without disclosing raw data, offering a new approach to addressing privacy and security concerns in the data exchange process of V2X. This paper proposes a group-by-aggregation query algorithm for data federation, aiming to protect personal privacy data while facilitating effective data sharing and analysis. The algorithm reverses the traditional group-by-aggregation queries process by not transmitting grouping results but rather passing encrypted aggregated attribute values to relevant data owners. By leveraging encryption algorithms with additive homomorphic or order-preserving properties to encrypt the aggregated attribute values, the algorithm ensures the correctness of mathematical operations performed under encryption, such as addition and comparison operations. Finally, the effectiveness and practicality of the algorithm are validated through experimental evaluations.