RISK: 高效处理加密地理文本数据上的丰富空间关键词查询
RISK: Efficiently processing rich spatial-keyword queries on encrypted geo-textual data
AI总结：
RISK通过新颖的k近邻四叉树实现高效加密地理文本数据的丰富空间关键词查询，提供安全性和扩展性，实验显示其性能显著优于现有方法。
摘要：
对地理文本数据的对称可搜索加密（SSE）已引起广泛关注。然而，现有方案依赖于任务特定且不兼容的索引，用于孤立的特定安全查询（例如范围或k近邻空间关键词查询），限制了实用性，因为多索引开销过高。为了解决这个问题，我们提出了RISK，一种用于加密地理文本数据上的丰富空间关键词查询的模型。采用文本优先再空间的方式，RISK基于一种新颖的k近邻四叉树（kQ-tree），该树嵌入了代表性区域的最近邻，kQ-tree进一步使用标准加密工具（如键哈希函数和对称加密）进行加密。总体而言，RISK无缝支持安全范围和k近邻查询，其安全性在IND-CKA2模型下可证明，并可扩展到多方场景和动态更新。在三个真实世界和一个合成数据集上的实验表明，RISK在1%范围查询和10近邻查询的响应时间上分别比最先进的方法快至少0.5到4个数量级。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
