| 基于灰关联的k匿名数据流隐私保护算法 |
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| 引用本文： | 张岐山,郭昆. 基于灰关联的k匿名数据流隐私保护算法[J]. 东北石油大学学报, 2012, 0(6): 93-100,12 |
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| 作者姓名： | 张岐山 郭昆 |
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| 作者单位： | 福州大学管理学院;福州大学数学与计算机科学学院 |
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| 基金项目： | 国家自然科学基金项目(70871024);福建省自然科学基金项目(2010J01358);福州大学科技发展基金项目(201-xy-16) |
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| 摘 要： | 与静态数据不同,数据流具有潜在无限、快速到达、变化频繁等特点,使得数据流隐私保护面临问题.在保证匿名要求的前提下,从降低信息损失和节约计算时间角度,提出一种基于灰关联的数据流隐私保护匿名算法(DSAoGRA),采用灰色关联度描述元组间的相似度,将元组划分成k匿名簇,实现数据流的k匿名化.数据实验结果表明,该算法在满足匿名要求的同时,比CASTLE算法具有较低的信息损失和较少的计算时间.
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| 关 键 词： | 数据流 隐私保护 k匿名 灰关联分析 |
Achieving k-anonymity privacy protection for data streams based on grey relational analysis |
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| ZHANG Qi-shan,GUO Kun. Achieving k-anonymity privacy protection for data streams based on grey relational analysis[J]. Journal of Northeast Petroleum University, 2012, 0(6): 93-100,12 |
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| Authors: | ZHANG Qi-shan GUO Kun |
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| Affiliation: | 1.School of Management,Fuzhou University,Fuzhou,Fujian 350108,China;2.College of Mathematics and Computer Science,Fuzhou University,Fuzhou,Fujian 350108,China) |
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| Abstract: | Data streams have the features of potential infinity,fast flowing and frequent variation,which are quite different from static data.Protecting the privacy in a data stream meets many new problems.An anonymity algorithm based on grey relational analysis for data streams is proposed in order to reduce information loss and computation time.The similarity between two tuples is described by the grey relational degree.K anonymized clusters are built upon the measure,which are used for data stream anonymization.The experiments conducted on the real data set demonstrate that the new method can achieve lower information loss and less runtime when compared with CASTLE. |
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| Keywords: | data stream privacy protection k-anonymity grey relational analysis |
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