Journal of Computer Applications ›› 2018, Vol. 38 ›› Issue (1): 67-72.DOI: 10.11772/j.issn.1001-9081.2017071863

Previous Articles     Next Articles

Hierarchical (αij, k, m)-anonymity privacy preservation based on multiple sensitive attributes

WANG Qiuyue, GE Lina, GENG Bo, WANG Lijuan   

  1. College of Information Science and Engineering, Guangxi University for Nationalities, Nanning Guangxi 530006, China
  • Received:2017-07-28 Revised:2017-09-02 Online:2018-01-10 Published:2018-01-22
  • Supported by:
    This work is partially supported by the National Natural Science Foundation of China (61462009), the Scientific Research Foundation of Guangxi University for Nationalities (2014MDYB029), the China-ASEAN Research Center of Guangxi University for Nationalities (Guangxi Science Experimental Center) 2014 Open Project (TD201404).


王秋月, 葛丽娜, 耿博, 王利娟   

  1. 广西民族大学 信息科学与工程学院, 南宁 530006
  • 通讯作者: 葛丽娜
  • 作者简介:王秋月(1991-),女,山东淄博人,硕士研究生,主要研究方向:信息安全;葛丽娜(1969-),女(毛南族),广西环江人,教授,博士,主要研究方向:信息安全;耿博(1990-),男,河北邢台人,硕士研究生,主要研究方向:信息安全;王利娟(1992-),女,山东临沂人,硕士研究生,主要研究方向:信息安全。
  • 基金资助:

Abstract: To resist existing limitations and associated attack by anonymization of single sensitive attributes, an (αij,k,m)-anonymity model based on greedy algorithm was proposed. Firstly, the (αij,k,m)-anonymity model was mainly to protect multi-sensitive attribute information. Secondly, the model for level was carried out according to the sensitive values of the sensitive attributes, if there were m sensitive attributes, there were m tables. Thirdly, each level was assigned a specific account αij by the model. Finally, the (αij,k,m)-anonymity algorithm based on greedy strategy was designed, and a local optimum method was adopted to implement the ideas of the model which improves the degree of data privacy protection. The proposed model was compared with other three models from information loss, execution times, and the sensitivity distance of equivalent class. The experimental results show that, although the execution time of the proposed model is slightly longer than other compared models, however, the information loss is less and the privacy protection degree of data is higher. It can resist the associated attack and protect the data of multi-sensitive attributes.

Key words: sensitive attribute, anonymization, associated attack, multi-sensitive attribute, (αij,k,m)-anonymity model

摘要: 针对单敏感属性匿名化存在的局限性和关联攻击的危害问题,提出了基于贪心算法的(αijkm)-匿名模型。首先,该(αijkm)-匿名模型主要针对多敏感属性信息进行保护;然后,该模型为每个敏感属性的敏感值进行分级设置,有m个敏感属性就有m个分级表;其次,并为每个级别设置一个特定的αij;最后,设计了基于贪心策略的(αijkm)匿名化算法,采取局部最优方法,实现该模型的思想,提高了对数据的隐私保护程度,并从信息损失、执行时间、等价类敏感性距离三个方面对4个模型进行对比。实验结果证明,该模型虽然执行时间稍长,但信息损失量小,对数据的隐私保护程度高,能够抵制关联攻击,保护多敏感属性数据。

关键词: 敏感属性, 匿名化, 关联攻击, 多敏感属性, (αij, k, m)-匿名模型

CLC Number: