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计算机系统应用英文版:2017,26(8):206-211
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基于KD树的信息发布隐私保护
(1.福建师范大学 软件学院, 福州 350108;2.福建省公共服务大数据挖掘与应用工程技术研究中心, 福州 350108)
KD Tree-Based Privacy Protection of Data Publishing
(1.Faculty of Software, Fujian Normal University, Fuzhou 350108, China;2.Fujian Engineering Research Center of Public Service Big Data Mining and Application, Fuzhou 350108, China)
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Received:December 11, 2016    
中文摘要: 随着医疗信息共享服务的发展,越来越多的患者病历信息被发布出来,敌手通过患者属性推断患者的隐私信息,从而造成患者隐私泄露.基于上述需求,提出基于KD树的隐私保护数据发布算法.利用KD树的性质,对每一维所在属性的泛化值进行分解,直到所有属性的泛化值不能分解,以确保每个叶子节点的所有属性的泛化值的区域达到最小,以减少信息损失.在对等价元组属性分解期间,对每个节点敏感属性值个数做l多样性约束,以降低隐私泄漏风险.实验结果表明,方案可以减少隐私泄露风险和信息损失.
Abstract:With the development of regional health information sharing services, an increasing number of patient records are released. However, the adversary can infer the patient’s privacy information through the patient’s attributes, thereby causing the patient’s privacy leakage. Based on the above requirements, a privacy protection data publishing algorithm based on KD tree is proposed. By the properties of KD-tree, the generalized value of each attribute is decomposed until the generalized value of all attributes cannot be decomposed to ensure that the generalized value of all attributes of each leaf node is minimized to reduce the information loss. During the decomposition of equivalent tuple attributes, the number of sensitive attribute values for each node is made to be a diversity constraint to reduce the risk of privacy leakage. The experimental results show that this scheme can reduce the risk of leakage of privacy, and information loss.
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基金项目:国家自然科学基金(61370078,61402109,61502102)
引用文本:
林国滨,姚志强,熊金波,林铭炜.基于KD树的信息发布隐私保护.计算机系统应用,2017,26(8):206-211
LIN Guo-Bin,YAO Zhi-Qiang,XIONG Jin-Bo,LIN Ming-Wei.KD Tree-Based Privacy Protection of Data Publishing.COMPUTER SYSTEMS APPLICATIONS,2017,26(8):206-211