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哈尔滨工程大学 计算机科学与技术学院,黑龙江 哈尔滨 150001
[ "王超(1988-),男,河北沧州人,哈尔滨工程大学博士生,主要研究方向为数据库与知识工程、数据挖掘、隐私保护。" ]
[ "杨静(1962-),女,黑龙江哈尔滨人,哈尔滨工程大学教授、博士生导师,主要研究方向为数据库与知识工程、数据挖掘、隐私保护、软件理论等。" ]
[ "张健沛(1956-),男,黑龙江哈尔滨人,哈尔滨工程大学教授、博士生导师,主要研究方向为企业智能计算、数据库与知识工程、数据挖掘、社会网络、软件理论等。" ]
[ "吕刚(1988-),男,吉林白城人,哈尔滨工程大学硕士生,主要研究方向为数据库与知识工程、数据挖掘、隐私保护。" ]
网络出版日期:2015-08,
纸质出版日期:2015-08-25
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王超, 杨静, 张健沛, 等. 基于投影区域密度划分的k匿名算法[J]. 通信学报, 2015,36(8):125-134.
Chao WANG, Jing YANG, Jian-pei ZHANG, et al. Algorithm for k-anonymity based on projection area density partition[J]. Journal on communications, 2015, 36(8): 125-134.
王超, 杨静, 张健沛, 等. 基于投影区域密度划分的k匿名算法[J]. 通信学报, 2015,36(8):125-134. DOI: 10.11959/j.issn.1000-436x.2015204.
Chao WANG, Jing YANG, Jian-pei ZHANG, et al. Algorithm for k-anonymity based on projection area density partition[J]. Journal on communications, 2015, 36(8): 125-134. DOI: 10.11959/j.issn.1000-436x.2015204.
在数据发布的隐私保护中,现有的算法在划分临时匿名组时,没有考虑临时匿名组中相邻数据点的距离,在划分过程中极易产生许多不必要的信息损失,从而影响发布匿名数据集的可用性。针对以上问题,提出矩形投影区域,投影区域密度和划分表征系数等概念,旨在通过提高记录点的投影区域密度来合理地划分临时匿名组,使划分后的匿名组产生的信息损失尽量小;并提出基于投影区域密度划分的k匿名算法,通过优化取整划分函数和属性维选择策略,在保证匿名组数量不减少的同时,减少划分过程中不必要的信息损失,进一步提高发布数据集的可用性。通过理论分析和实验验证了算法的合理性和有效性。
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.
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