Optimized Space-Filling Curve-Driven Forward-Secure Range Query on Location-Related Data for Unmanned Aerial Vehicle Networks
Zhen Lv, Xin Li, Yanguo Peng, Jin Huang · Electronics · 2025
Unmanned aerial vehicle networks (UAVNs) are widely used to collect various location-related data, with applications ranging from military reconnaissance to the low-altitude economy. Data security and privacy are critical concerns when outsourcing location-related data to a public cloud. To alleviate these concerns, location-related data are encrypted before outsourcing to the public cloud. However, encryption decreases the operability of the outsourced encrypted data; thus, unmanned aerial vehicles cannot operate on the encrypted data directly. Among operations on encrypted location-related data, the forward-secure range query is one of the most fundamental operations. In this paper, we present a forward-secure range query based on spatial division to achieve a highly efficient range query on encrypted location-related data while preserving both data security and privacy. Specifically, various space-filling curves were experimentally investigated for both the range query and the k-nearest-neighbor query. Then, a forward-secure range query (namely, OSFC-FSQ) was constructed on an encrypted dual dictionary. The proposed scheme was evaluated on real-world datasets, and the results show that it outperforms state-of-the-art schemes in terms of accuracy and query time in the cloud.