Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
4944697 | Information Sciences | 2017 | 19 Pages |
Abstract
In this work, we present a novel model to remove the above limitations. Specifically, a reusable and single-interactive SANN paradigm is proposed in Euclidean high-dimensional space. Firstly, we present a secure variation of B+-tree (i.e., Bc-tree) to quickly locate high-dimensional candidates in cloud by leveraging on comparable encryption. Based on that, an arbitrary query requestor acquires approximate k-nearest neighbors by linearly scanning over candidates. Meanwhile, two refinements, multi-index strategy and boosting refinement strategy, are proposed to further improve the accuracy of search result and overcome the high-dependency of bandwidth, respectively. In the end, through extensive evaluations on four data sets, the proposed mechanisms are demonstrated to be superior in the tradeoff between accuracy and security.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Yanguo Peng, Jiangtao Cui, Hui Li, Jianfeng Ma,