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| 基于取整划分函数的k 匿名算法 |
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吴英杰1,2,3, 唐庆明1, 倪巍伟2, 孙志挥2
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1.福州大学 数学与计算机科学学院,福建 福州 350108;2.东南大学 计算机科学与工程学院,江苏 南京 210096;3.网络系统信息安全福建省高校重点实验室(福州大学),福建 福州 350108
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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2k2 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. |
| 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 |
| DOI：10.3724/SP.J.1001.2012.04157 |
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| 基金项目:国家自然科学基金(61003057); 福建省自然科学基金(2010J01330) |
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| Algorithm for k-Anonymity Based on Rounded Partition Function |
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WU Ying-Jie1,2,3, TANG Qing-Ming1, NI Wei-Wei2, SUN Zhi-Hui2
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1.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350108, China;2.College of Computer Science and Engineering, Southeast University, Nanjing 210096, China;3.Key Laboratory of Network System Information Security (Fuzhou University), Depart
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| Abstract: |
| 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. |
| Key words: privacy preservation data publishing algorithm for k-anonymity rounded partition function upper bound on size of anonymization group |
