| Abstract | As the proliferation of smart vehicles has fostered an abundance of real-time data, various data analysis tools, such as aggregation queries, are expected to be deployed to extract insights and make transportation systems much smarter. Meanwhile, to cope with the growing service scale, edge servers are employed to collect data and deliver the service, which however provokes privacy concerns related to the reported data and user queries. Previously reported solutions on privacy-preserving aggregation queries focus on static datasets or require data persistence, leading to storage pressure and slower query responses. In this paper, we propose a privacy-preserving dynamic aggregation query scheme using edge servers, specifically addressing the problem of online aggregation queries. By combining homomorphic encryption and predicate encryption, our scheme enables the edge server to aggregate real-time data and respond to queries, safeguarding sensitive information from vehicles and data users. The integration of advanced cryptographic primitives ensures data and query privacy and integrity. Comprehensive theoretical analyses demonstrate our scheme's effectiveness in privacy preservation, boasting a manageable computational and communication overhead. The scheme, thus, presents a practical solution for privacy-preserving dynamic aggregation queries, fulfilling an unmet need in real-time transportation systems. |
|---|
