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基于取整划分函数的k匿名算法 被引量：8

Algorithm for k-Anonymity Based on Rounded Partition Function
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摘要 提出一种基于取整划分函数的K匿名算法,并从理论上证明该算法在非平凡的数据集中可以取得更低的上界.特别地,当数据集大于2k^2时,该算法产生的匿名化数据的匿名组规模的上界为k+1;而当待发布数据表足够大时,算法所生成的所有匿名组的平均规模将足够趋近于K.仿真实验结果表明,该算法是有效而可行的. This paper proposes an algorithm based on rounded partition function for k-anonymity. By rigorous theoretical proof, the study will show that a better upper bound on size of the anonymization groups can be obtained in non-trivial data sets. In particular, when the size of the original dataset is greater than 2k2, the upper bound will be reduced to k＋1. Further, the average size of all anonymization groups of the anonymous data will be close enough to k when the size of the original dataset is large enough. Experimental results on real datasets show that this algorithm is effective and feasible.
作者 吴英杰 唐庆明 倪巍伟 孙志挥
机构地区 福州大学数学与计算机科学学院 东南大学计算机科学与工程学院 网络系统信息安全福建省高校重点实验室(福州大学)
出处 《软件学报》 EI CSCD 北大核心 2012年第8期2138-2148,共11页 Journal of Software
基金 国家自然科学基金(61003057) 福建省自然科学基金(2010J01330)
关键词 隐私保护 数据发布 k匿名算法 取整划分函数 匿名组规模上界 privacy preservation data publishing algorithm for k-anonymity rounded partition function upper bound on size of anonymization group
分类号 TP309 [自动化与计算机技术—计算机系统结构]
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参考文献21

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同被引文献66

  • 1张鹏,童云海,唐世渭,杨冬青,马秀莉.一种有效的隐私保护关联规则挖掘方法[J].软件学报,2006,17(8):1764-1774. 被引量：53
  • 2Sweeney L. k-anonymity: a model for protecting privacy[J]. Int J Uncertain Fuzz, 2002, 10 (5): 557.
  • 3Samarati P, Sweeney L. Protecting privacy when disclosing information..kIAnonymity and its enforce- ment through generalization and suppression [R/ OL]. (1998-04-20). [2013-08-23] . http..//www. cse. iitb. ac. in/dbms/Data/Courses/CS632/2007/ Papers/kanon-samarati. pdf.
  • 4Sweeney L. Achieving k-anonymity privacy protec- tion using generalization and suppression[J]. Int J Uncertain Fuzz, 2002, 10(5), 571.
  • 5Samarati P, Sweeney L. Generalizing data to pro- vide anonymity when disclosing information [C]// Proceedings of the 17th ACM SIGMOD SIGACT- SIGART Symposium on the Principles of Database Systems. Seattle, WA, USA:ACM Press, 1998.
  • 6Terrovitis M, Mamoulis N, Kalnis P. Anonymity in unstructured data[R]. Hong Kong: Hong Kong U- niversity, 2008.
  • 7Sweeney L. Guaranteeing anonymity when sharing medical data, the Datafly system[C]. Washington, DC.. Hanley & Belfus, Inc, 1997: 51.
  • 8LeFevre K, DeWitt D J, Ramakrishnan R. Incogni- to: efficient full-domain k-anonymity [ C]//ACM SIGMOD International Conference on Management of Data. Baltimore, USA.. ACM Press, 2005.
  • 9LeFevre K, DeWitt D J, Ramakrishnan R. Mondri- an multidimensional K-Anonymity [C]. Washing- ton, USA: IEEE Computer Society, 2006.
  • 10Meyerson A, Williams R. On the complexity of op- timal K-anonymity[C]// Proceedings of the 23th ACM SIGMOD SIGACT-SIGART Symposium on the Principles of Database Systems. New York, USA:ACM Press, 2004.

引证文献8

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软件学报
2012年 第8期

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