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.

翻译：面向地理文本数据的对称可搜索加密技术已引起广泛关注。然而，现有方案依赖于针对孤立特定安全查询（例如范围或k近邻空间关键词查询）的任务专用且互不兼容的索引，由于多索引开销过高而限制了实用性。为解决此问题，我们提出RISK模型，用于在加密地理文本数据上执行富空间关键词查询。RISK采用"先文本后空间"的处理方式，基于一种新颖的k近邻四叉树构建，该树嵌入了代表性及区域最近邻信息，并利用标准密码学工具（例如密钥哈希函数与对称加密）对kQ树进行加密。总体而言，RISK无缝支持安全范围查询与k近邻查询，在IND-CKA2模型下具备可证明安全性，并可扩展至多参与方场景与动态更新环境。在三个真实数据集与一个合成数据集上的实验表明，对于1%范围查询与10近邻查询，RISK的响应时间分别比现有最优方法至少快0.5和4个数量级。