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Journal of Software:2017.28(2):341-351

面向表数据发布隐私保护的贪心聚类匿名方法
姜火文,曾国荪,马海英
(同济大学 计算机科学与技术系, 上海 200092;江西科技师范大学 数学与计算机科学学院, 江西 南昌 330038;嵌入式系统与服务计算教育部重点实验室(同济大学), 上海 200092;同济大学 计算机科学与技术系, 上海 200092;嵌入式系统与服务计算教育部重点实验室(同济大学), 上海 200092)
Greedy Clustering-Anonymity Method for Privacy Preservation of Table Data-Publishing
JIANG Huo-Wen,ZENG Guo-Sun,MA Hai-Ying
(Department of Computer Science and Technology, Tongji University, Shanghai 200092, China;School of Mathematics and Computer Science, Jiangxi Science & Technology Normal University, Nanchang 330038, China;Key Laboratory of Embedded System and Service Computing, Ministry of Education(Tongji University), Shanghai 200092, China;Department of Computer Science and Technology, Tongji University, Shanghai 200092, China;Key Laboratory of Embedded System and Service Computing, Ministry of Education(Tongji University), Shanghai 200092, China)
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Received:February 11, 2015    Revised:November 06, 2015
> 中文摘要: 为了防范隐私泄露,表数据一般需要匿名处理后发布.现有匿名方案较少分类考察准标识属性概化,并缺少同时考虑信息损失量和时间效率的最优化.利用贪心法和聚类划分的思想,提出一种贪心聚类匿名方法:分类概化准标识属性,并分别度量其信息损失,有利于减小并合理评价信息损失.对元组间距离和元组与等价类距离,建立与最小合并概化信息损失值正相关的距离定义,聚类过程始终选取具有最小距离值的元组添加,从而保证信息损失总量趋于最小.按照k值控制逐一聚类,实现等价类均衡划分,减少了距离计算总量,节省了运行时间.实验结果表明,该方法在减少信息损失和运行时间方面是有效的.
Abstract:To prevent privacy disclosure, table data generally needs to be anonymized before being published. Existing anonymity methods seldom distinguish different types of quasi-identifier in generalization, and also lack investigation into optimization of both information loss and time efficiency. In this paper, a greedy clustering-anonymity method is proposed using the ideas of greedy algorithm and clustering algorithm. The method makes distinct generalizations according to the type of quasi-identifier to conduct different calculations on information loss, and this providing reduction and reasonable estimate on information loss. Moreover, with regard to distance between tuples, or distance between a tuple and an equivalence class, two definitions are put forward in order to achieve minimum information loss in merging generalization. When establishing a new cluster, the tuple with the minimum distance in the ongoing cluster is always chosen to add. It ensures that the total information loss is close to minimum. Since the number of tuples in establishing each cluster is subject to k and the size of every cluster is equal to or just greater than k, the amount of calculation on distances and therefore the running time are reduced. Experimental results show that the proposed method is effective in reducing both information loss and running time.
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基金项目:华为创新研究计划(IRP-2013-12-03);高效能服务器和存储技术国家重点实验室开放基金(2014HSSA10);江西科技师范大学重点科研项目(2016XJZD002) 华为创新研究计划(IRP-2013-12-03);高效能服务器和存储技术国家重点实验室开放基金(2014HSSA10);江西科技师范大学重点科研项目(2016XJZD002)
Foundation items:Huawei Innovation Research Project (IRP-2013-12-03); Program of State Key Laboratory of High-End Server & Storage Technology (2014HSSA10); Key Research Project of Jiangxi Science and Technology Normal University (2016XJZD002)
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姜火文,曾国荪,马海英.面向表数据发布隐私保护的贪心聚类匿名方法.软件学报,2017,28(2):341-351

JIANG Huo-Wen,ZENG Guo-Sun,MA Hai-Ying.Greedy Clustering-Anonymity Method for Privacy Preservation of Table Data-Publishing.Journal of Software,2017,28(2):341-351