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COP

机译:警察

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摘要

Database-as-a-Service (DAS) is an emerging database management paradigm wherein partition based index is an effective way to querying encrypted data. However, previous research either focuses on one-dimensional partition or ignores multidimensional data distribution characteristic, especially sparsity and locality. In this paper, we propose Cluster based Onion Partition (COP), which is designed to decrease both false positive and dead space at the same time. Basically, COP is composed of two steps. First, it partition covered space level by level, which is like peeling of onion; second, at each level, a clustering algorithm based on local density is proposed to achieve local optimal secure partition. Extensive experiments on real dataset and synthetic dataset show that COP is a secure multidimensional partition with much less efficiency loss than previous top down or bottom up counterparts.
机译:数据库即服务(DAS)是一种新兴的数据库管理范例,其中基于分区的索引是查询加密数据的有效方法。但是,先前的研究要么集中于一维分区,要么忽略了多维数据分布特征,尤其是稀疏性和局部性。在本文中,我们提出了基于簇的洋葱分区(COP),其旨在同时减少误报和死空间。 COP基本上包括两个步骤。首先,它将覆盖的空间逐层划分,就像洋葱去皮一样。其次,在每个层次上,提出了一种基于局部密度的聚类算法,以实现局部最优的安全分区。在真实数据集和合成数据集上的大量实验表明,COP是一个安全的多维分区,其效率损失比以前的自上而下或自下而上的同行要少得多。

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