Verifiable Learned PR-Tree Indexing for Privacy-Preserving Range Queries Over Encrypted Geospatial Data

Protecting the privacy of geospatial data, along with efficient encrypted query processing, remains a major challenge in cloud-based GIS applications and location-based applications (LBS). In this study, a privacy-preserving framework for secure range query processing over encrypted vector geospatial data using a learned PR-tree index is integrated with an XGBoost-based bucket prediction model. In the first phase, the framework employs a lightweight dual-encryption scheme based on Lorentz and Galilean transformations. This encryption preserves coordinate relationships and enables reversible coordinate recovery. To improve query execution efficiency in the encrypted domain, the learned PR-tree predicts the most probable PR-tree buckets. This minimizes unnecessary search path traversal. Further, integrity verification during storage and query processing is ensured using the Merkle Hash Root and EdDSA digital signatures. Experimental evaluation was conducted using various real-world point, polyline, and polygon datasets. The proposed framework achieved prediction accuracy up to 97.2% with a very low mean bucket prediction error of 0.028. The obtained results demonstrate the efficiency and practicality of the proposed framework over encrypted cloud data.

@article{Aher2026,
  title     = {Verifiable Learned PR-Tree Indexing for Privacy-Preserving Range Queries Over Encrypted Geospatial Data},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {6},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Anagha Aher and Sangita Chaudhari},
  doi       = {10.14569/IJACSA.2026.0170619},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170619}
}