Hao Li

Secure and efficient spatial range query over encrypted outsourced vector data remains a challenging issue, particularly for polyline and polygon data. Existing schemes primarily focus on point data and have not been properly extended to more complex spatial objects. Therefore, we propose a secure and efficient spatial range query scheme that supports multi-type encrypted vector data. The data owner first outsources encrypted vector data and the encrypted R-tree index to a cloud server, and then the cloud server retrieves matching data identifiers using an encrypted query rectangle and encrypted index. After that, the cloud server accesses the spatial database to obtain the corresponding encrypted vector data. Finally, the query user decrypts the encrypted vector data and filters with a query rectangle to get actual query results. Experiments show that our scheme achieves query efficiency comparable to that of query based on a plaintext index, while providing enhanced query security at the cost of only 3.97% performance overhead. We also compared our point data query with existing scheme. And the results indicate that our scheme effectively reduces false positives and improves query efficiency by enabling intersection tests between the query rectangle and the minimum bounding rectangles of spatial objects in the encrypted domain. Overall, our scheme can support spatial range query over multi-type encrypted vector data, achieving a balance between security and efficiency.