Secure Dynamic and Verifiable Skyline Query for Low-Altitude Economy
With the rapid development of the low-altitude economy, skyline queries play a crucial role in identifying relevant data based on specific query requirements. To reduce storage overhead and improve query efficiency, data owners increasingly outsource their data to cloud servers. However, cloud servers may be untrusted and can potentially return incorrect or incomplete query results. Furthermore, most existing skyline query schemes do not support dynamic data updates and fail to satisfy the privacy and verifiability requirements essential for real-world low-altitude economic scenarios. In this paper, we propose a Secure Dynamic and Verifiable Skyline Query (SDVSQ) scheme, which supports dynamic and verifiable searchable encryption for skyline queries. We first devise a novel index structure, SDVR-tree, designed for efficient skyline query processing, where each data object is represented as a linked list in the leaf nodes. Each node in the linked list corresponds to a raw data object encrypted using a modified Paillier cryptosystem, ensuring that users accessing a list node cannot infer its sub-nodes. To support secure skyline computation, we design privacy-preserving protocols for squared Euclidean distance, comparison, and minimum operations. Additionally, SDVSQ ensures public verifiability of query result correctness and completeness by leveraging blockchain to store verification objects, while avoiding heavy on-chain computation. Formal security analysis shows that SDVSQ ensures forward privacy, data privacy, and query privacy while supporting result verification. Extensive experimental results demonstrate that SDVSQ significantly reduces computational overhead for both updates and skyline queries.
Authors
- Qin Jiang (ORCID: https://orcid.org/0000-0002-0689-8304)
- Di Zhang
- Zhangjie Fu (ORCID: https://orcid.org/0000-0002-4363-2521)
- Fuyuan Song (ORCID: https://orcid.org/0000-0002-0784-7007)
- Chuan Zhang (ORCID: https://orcid.org/0000-0001-7684-8540)
- Xiaowei Sun
- Yi Wu (ORCID: https://orcid.org/0009-0001-1586-3226)
Institutions
- Beijing University of Posts and Telecommunications (CN)
- Nanjing University of Information Science and Technology (CN)
- China Academy of Information and Communications Technology (CN)
- Ministry of Industry and Information Technology (CN)
Publication Details
- Journal
- IEEE Internet of Things Journal
- Published
- 2025-09-01
- DOI
- https://doi.org/10.1109/jiot.2025.3604485
- Primary Topic
- Data Management and Algorithms
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- China Postdoctoral Science Foundation