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

2026-02-24

AI Summary

Existing searchable encryption schemes struggle to efficiently support diverse spatial keyword queries due to their reliance on task-specific and mutually incompatible index structures, resulting in high overhead and limited practicality. This work proposes RISK, a novel framework featuring a unified index structure—the k-nearest neighbor quadtree (kQ-tree)—which adopts a “text-first, space-second” strategy to seamlessly support both secure range queries and k-nearest neighbor queries. Built upon symmetric encryption and keyed hash functions, RISK offers provable security under the IND-CKA2 model, along with dynamic update capabilities and extensibility to multi-party settings. Experimental evaluations on real-world and synthetic datasets demonstrate that RISK achieves at least half an order of magnitude faster response times for 1% range queries and four orders of magnitude improvement for 10-nearest neighbor queries compared to the state-of-the-art methods.

Technology Category

• Search and Optimization: Distributed Search
• Data Mining & Knowledge Management: Intelligent Query Processing
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Security and Privacy: Applications of cryptography
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Web crawling and indexing

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.

Problem

• symmetric searchable encryption
• geo-textual data
• spatial-keyword queries
• range queries
• k-nearest neighbor queries

Innovation

• symmetric searchable encryption
• spatial-keyword query
• k-nearest neighbor quadtree
• encrypted geo-textual data
• dynamic secure indexing