RISK: Efficiently processing rich spatial-keyword queries on encrypted geo-textual data
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866912923279949824 |
|---|---|
| author | Lv, Zhen Cao, Cong Huo, Hongwei Cui, Jiangtao Peng, Yanguo Li, Hui Liu, Yingfan |
| author_facet | Lv, Zhen Cao, Cong Huo, Hongwei Cui, Jiangtao Peng, Yanguo Li, Hui Liu, Yingfan |
| contents | Symmetric searchable encryption (SSE) for geo-textual data has attracted significant attention. However, existing schemes rely on task-specific, incompatible indices for isolated specific secure queries (e.g., range or k-nearest neighbor spatial-keyword queries), limiting practicality due to prohibitive multi-index overhead. To address this, we propose RISK, a model for rich spatial-keyword queries on encrypted geo-textual data. In a textual-first-then-spatial manner, RISK is built on a novel k-nearest neighbor quadtree (kQ-tree) that embeds representative and regional nearest neighbors, with the kQ-tree further encrypted using standard cryptographic tools (e.g., keyed hash functions and symmetric encryption). Overall, RISK seamlessly supports both secure range and k-nearest neighbor queries, is provably secure under IND-CKA2 model, and extensible to multi-party scenarios and dynamic updates. Experiments on three real-world and one synthetic datasets show that RISK outperforms state-of-the-art methods by at least 0.5 and 4 orders of magnitude in response time for 1% range queries and 10-nearest neighbor queries, respectively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_20952 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | RISK: Efficiently processing rich spatial-keyword queries on encrypted geo-textual data Lv, Zhen Cao, Cong Huo, Hongwei Cui, Jiangtao Peng, Yanguo Li, Hui Liu, Yingfan Databases Symmetric searchable encryption (SSE) for geo-textual data has attracted significant attention. However, existing schemes rely on task-specific, incompatible indices for isolated specific secure queries (e.g., range or k-nearest neighbor spatial-keyword queries), limiting practicality due to prohibitive multi-index overhead. To address this, we propose RISK, a model for rich spatial-keyword queries on encrypted geo-textual data. In a textual-first-then-spatial manner, RISK is built on a novel k-nearest neighbor quadtree (kQ-tree) that embeds representative and regional nearest neighbors, with the kQ-tree further encrypted using standard cryptographic tools (e.g., keyed hash functions and symmetric encryption). Overall, RISK seamlessly supports both secure range and k-nearest neighbor queries, is provably secure under IND-CKA2 model, and extensible to multi-party scenarios and dynamic updates. Experiments on three real-world and one synthetic datasets show that RISK outperforms state-of-the-art methods by at least 0.5 and 4 orders of magnitude in response time for 1% range queries and 10-nearest neighbor queries, respectively. |
| title | RISK: Efficiently processing rich spatial-keyword queries on encrypted geo-textual data |
| topic | Databases |
| url | https://arxiv.org/abs/2602.20952 |