RISK: Efficiently processing rich spatial-keyword queries on encrypted geo-textual data

TL;DR

RISK is a novel encryption scheme enabling efficient, secure processing of complex spatial-keyword queries on geo-textual data, overcoming limitations of prior task-specific approaches.

Contribution

It introduces a unified, encrypted index structure (kQ-tree) supporting multiple query types with provable security and high efficiency, improving over existing methods.

Findings

Outperforms state-of-the-art in response time by up to 4 orders of magnitude.

Supports secure range and k-NN queries on encrypted data.

Extensible to multi-party and dynamic update scenarios.

Abstract

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.