摘要：

针对现有空间离群点检测方法难以同时保证数据安全性和检测结果有效性的问题,提出一种隐私保护的空间离群点检测方法。该方法基于空间邻域行为属性值的统计结果及马哈拉诺比斯距离进行空间离群点的检测,通过对基于半诚实模型的安全多方距离、合并向量的中位数及标准化等计算协议的定义和应用,实现私有信息的保护。实验结果表明,该方法在保护隐私信息的同时保证了检测结果的准确性。

关键词: 空间离群点检测, 空间邻域, 空间属性, 非空间属性, 安全多方计算

Abstract: Foucusing on the issue that the existing spatial outlier detection methods fail to effectively solve the problem of guaranteeing both the data security and the validity of detection results at the same time,a privacy-preserving spatial outlier detection method is proposed,which uses statistical results of behavior attributes in spatial neighborhood and Mahalanobis distance to detect spatial outliers,and protects the privacy information by using the secure multi-party computation protocol based on semi-honest model,including secure distance computation,secure median computation of the combined vector and secure standardization protocols.Experimental results show that the method guarantees both the ability of privacy preserving and the effect of spatial outlier detection.

Key words: spatial outlier detection, spatial neighborhood, spatial attribute, non-spatial attribute, Secure Multi-party Computation(SMC)

中图分类号: 

• TP391