数字图书馆个性化匿名发布方法

针对数字图书馆数据发布中的用户隐私保护,提出一种个性化的匿名方法。用户主体设置属性的敏感因子,通过数据属性之间的关联规则设置属性权重,由此得到的用户信息隐私保护度对数据集进行划分和匿名,从而实现用户个性化匿名保护。结果表明,结合属性的权重得到的个性化参数更加贴合实际的数据关系,减小用户由于个性化设置造成的"过分"保护,同时提高数据发布质量。

A personalized anonymous publishing method in digital library

Aiming at the user privacy protection technology in released digital library data, we propose a personalized anonymous method. The user sets sensitive factors by themselves, and assigns attribute weights according to association rules among data attributes, thus obtaining the user information privacy protection degree of the data. We utilize this degree to conduct data division and anonymity, and realize personalized anonymous protection for the user. Experimental results show that the personalized parameters obtained through the weights of attributes can better reflect actual data relationship, decrease "excessive" protection caused by users' personalized settings, and in the meantime improve data publishing quality.