【摘要】 为权衡敏感或重要数据的公开及关键内容的隐含间的关系,提出一种基于变精度粗糙集模型中的β重要度和粒度原理的数据隐含方法,通过获取信息表中的重要属性,对次要属性的属性值进行扩展,从而使用户信息的粒度变粗,达到对信息表的数据隐含化效果。仿真实验结果表明,该方法是可行的。更多还原

【Abstract】 In order to balance the relation between publicity of sensitive or important information and the anonymisation of privacy information, This paper proposes the β-importance based on Variable Precision Rough Set(VPRS) model and granularity theory to anonymise data.The important attributes are obtained from the information table, and the values of unimportant attributes are handled to extend.In term of users, the granularity of information becomes rough, and the anonymisation is achieved.Simulation experimental results show this method is feasible.更多还原