文章基本信息

标题：Privacy Preserving Similarity Measurement
本地全文：下载
作者：ZHANG Guo-rong
期刊名称：International Journal of Wireless and Microwave Technologies(IJWMT)
印刷版ISSN：2076-1449
电子版ISSN：2076-9539
出版年度：2011
卷号：1
期号：4
页码：27-34
出版社：MECS Publisher
摘要：Data similarity measurement is an important direction for data mining research. This paper is concentrated on the issue of protecting the underlying attribute values when sharing data for the similarity of objects measurement and proposes a simple data transformation method: Isometric-Based Transformation (IBT). IBT selects the attribute pairs and then distorts them with Isometric Transformation. In the process of transformation, the goal is to find the proper angle ranges to satisfy the least privacy preserving requirement and then randomly choose one angle in this interval. The experiment demonstrates that the method can distort attribute values, preserve privacy information and guarantee valid similarity measurement.
关键词：Privacy Preserving; Similarity; Isometric Transformation