基于投影区域密度划分的k匿名算法

doi:10.11959/j.issn.1000-436x.2015204

Abstract

Abstract:

In data publishing privacy preserving,while classifying temporary anonymous groups,the existing algorithms didn’t consider the distance between adjacent data points,and could easily produce a lot of unnecessary information loss,thus affecting the availability of released anonymous data sets.To solve the above problem,the concept of rectangular projection area,the projection area density and partition coefficient characterization were presented,aim to increase the recording points’s projection area density to divide temporary anonymous group reasonably,and to make the information loss of divided anonymous groups as small as possible.And presents the algorithm for k-anonymity based on projection area density partition,by optimizing the rounded partition function and properties dimension selection strategy,to reduce unnecessary information loss and to further improve the availability of released data sets,without reducing the number of anonymous groups.The rationality and validity of the algorithm are verified by theoretical analysis and multiple experiments.

Key words: privacy preserving, temporary anonymous group, rectangular projection area, projection area density

Chao WANG,Jing YANG,Jian-pei ZHANG,Gang LV. Algorithm for k-anonymity based on projection area density partition[J]. Journal on Communications, 2015, 36(8): 125-134.

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References 17

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数据集名称	别名	属性数目	记录数目	数据类型
输血服务中心数据集	blood	3	499	实数
图像数据集	image	6	2 100	实数
字符识别数据集	recognition_2 000	10	2 171	实数
	recognition_20 000	10	18 431	实数

Algorithm for k-anonymity based on projection area density partition

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