面向数据发布的隐私保护模型及参数优选方法

为更好地对待发布数据进行隐私保护，构建综合k-匿名、l-多样性和t-闭合方法的匿名化隐私保护模型。利用该模型能够选择最适合的隐私保护方法，并优选对应的隐私保护参数，达到数据提供者所期望的隐私保护效果，满足数据使用者对可用性的要求。实验结果表明，该方法不仅可以找到相对较优的参数值，而且能够有效满足具有不同身份和应用需求的用户对数据发布的要求。

Privacy Protection Model and Parameter Optimization Method for Data Dissemination

In order to improve the privacy protection for data to be published,this paper proposes a privacy protection model integrating k-anonymity,l-diversity and t-closure methods.The model can assist in the selection of the most suitable privacy protection method,and the optimization of the corresponding privacy protection parameters,meeting the privacy protection requirements of data providers and availability requirements of data consumers.Experimental results show that the proposed method can find relatively optimized parameter values,and satisfy users with different identities and application requirements for data dissemination.