Set similarity query is a fundamental query type in various applications, such as clinical diagnosis, online shopping, and mobile crowdsensing. Meanwhile, as the prevalence of outsourced query services, privacy-preserving set similarity query has been considerablely studied. However, to the best of our knowledge, most previously reported solutions suffer from applicability, efficiency, or security issues. Aiming at addressing these issues, we propose an efficient and privacy-preserving set similarity range query scheme (EPSet), where Jaccard similarity is employed as the similarity metric. Specifically, the set similarity range query is first transformed into multi-dimensional range queries by leveraging the triangle inequality of Jaccard distance. Then, a pivot-based k-d tree is designed for indexing the dataset and processing the set similarity query. After that, we design homomorphic encryption based privacy-preserving filter/refinement protocols, respectively named as PPF and PPR, to protect set similarity query privacy, and propose our EPSet scheme. The security of our scheme is proved under the simulation-based real/ideal model, and the performance is validated thorugh the extensive experiment evaluation.

中文翻译：

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EPSet：加密数据上的高效且保护隐私的集合相似性范围查询

集合相似度查询是临床诊断、在线购物、移动群智感知等多种应用中的基本查询类型。同时，随着外包查询服务的盛行，隐私保护的集合相似性查询得到了广泛的研究。然而，据我们所知，大多数先前报告的解决方案都存在适用性、效率或安全性问题。针对这些问题，我们提出了一种高效且保护隐私的集合相似度范围查询方案（EPSet），其中采用杰卡德相似度作为相似度度量。具体来说，首先利用杰卡德距离的三角不等式将集合相似度范围查询转化为多维范围查询。然后，设计基于枢轴的kd树来索引数据集并处理集合相似性查询。之后，我们设计了基于同态加密的隐私保护过滤/细化协议，分别称为PPF和PPR，以保护集合相似性查询隐私，并提出了我们的EPSet方案。我们的方案的安全性在基于仿真的真实/理想模型下得到了证明，并且通过大量的实验评估验证了性能。