Achieving Secure and Dynamic Range Queries Over Encrypted Cloud Data
Achieving Secure and Dynamic Range Queries Over Encrypted Cloud Data
复制标题
实现加密云数据的安全动态范围查询
DOI：
10.1109/tkde.2020.2983030
复制
发表时间：
2022-01
影响因子：
8.9
通讯作者：
Liusheng Huang
中科院分区：
文献类型：
--
作者：
Wei Yang;Yangyang Geng;Lu Li;Xike Xie;Liusheng Huang
云计算正在促使数据所有者将其数据库外包到云端。然而，出于隐私考虑，敏感数据在外包之前必须进行加密，这不可避免地给数据的有效利用带来了挑战。现有的工作要么侧重于关键词搜索，要么存在安全保障不足或效率低下的问题。在本文中，我们专注于动态加密云数据的多维范围查询。我们首先提出了一种基于树的动态加密云数据私有范围查询方案（TRQED），该方案支持比线性范围查询更快的速度并保护单维隐私。然后，我们讨论TRQED在隐私保护方面的缺陷。我们修改了系统框架，采用双服务器模型，提出了一种更安全的范围查询方案，称为TRQED<inline-formula><tex-math notation="LaTeX">$^{+}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq1-2983030.gif"/></alternatives></inline-formula>。通过新设计的安全节点查询（SNQ）和安全点查询（SPQ），我们提出了基于扰动的不经意的R树遍历（ORT）操作，以保留路径模式和更强的单维隐私。最后，我们对现实世界的数据集进行全面的实验，并与现有的工作进行比较，以评估所提出方案的性能。实验结果表明，我们的 TRQED 和 TRQED<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq2-2983030.gif"/></alternatives></inline-formula>在隐私保护水平和效率方面超越了最先进的方法。
Cloud computing is motivating data owners to outsource their databases to the cloud. However, for privacy concerns, the sensitive data has to be encrypted before outsourcing, which inevitably posts a challenging task for effective data utilization. Existing work either focuses on keyword searches, or suffers from inadequate security guarantees or inefficiency. In this paper, we concentrate on multi-dimensional range queries over dynamic encrypted cloud data. We first propose a tree-based private range query scheme over dynamic encrypted cloud data (TRQED), which supports faster-than-linear range queries and protects single-dimensional privacy. Then, we discuss the defects of TRQED in terms of privacy-preservation. We modify the framework of the system by adopting a two-server model and put forward a safer range query scheme, called TRQED<inline-formula><tex-math notation="LaTeX">$^{+}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq1-2983030.gif"/></alternatives></inline-formula>. By newly designed secure node query (SNQ) and secure point query (SPQ), we propose the perturbation-based oblivious R-tree traversal (ORT) operation to preserve both path pattern and stronger single-dimensional privacy. Finally, we conduct comprehensive experiments on real-world datasets and perform comparisons with existing works to evaluate the performance of the proposed schemes. Experimental results show that our TRQED and TRQED<inline-formula><tex-math notation="LaTeX">$^+$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mo>+</mml:mo></mml:msup></mml:math><inline-graphic xlink:href="yang-ieq2-2983030.gif"/></alternatives></inline-formula> surpass the state-of-the-art methods in privacy-preservation level and efficiency.