计算机工程与科学2011，Vol.33Issue(9)：7-12,6.DOI:10.3969/j.issn.1007-130X.2011.09.002
K-匿名隐私保护模型中不确定性数据的建模问题研究
Modeling the Uncertain Data in the K-Anonymity Privacy Protection Model
摘要
Abstract
Modeling is the basis for the data management of uncertainty. The specificity in the uncertainty of the data in the k-anonymity privacy protection model is found, namely, its uncertainty is caused by artificial generalization, and the probability that each instance is reduced after generalization to the o-riginal tuple is equal. Because of its specificity, the past modeling approaches of uncertainty data are not suitable for the uncertainty data in the k-anonymity privacy protection model simply. In order to describe uncertainty data in the k-anonymity privacy protection model, several new modeling methods are proposed in this paper: the Kattr model uses the attribute-ors ways to describe the uncertainty in the quasi-I-dentifier attribute values of the k-anonymity privacy protection model; the Ktuptr model takes the quasi-I-dentifier attribute values as relations and use the tuple-ors ways to describe the relations; the Kupperiower model separates some generalization values to two fields: the upper limit and the lower limit; the Klre. Model based on the property that k-anonymous table is the generalization of the ordinary relation with generalization tree splits the quasi-identifier attribute value into a certain tree reversely. A model space which consists of these models is given. The completeness and closure about these models are discussed later.关键词
建模/不确定性数据/K-匿名/模型空间/完备性/封闭性Key words
modeling/ uncertain data/ k-anonymity/ model space completeness/ closure分类
信息技术与安全科学引用本文复制引用
吴佳伟,刘国华,王梅..K-匿名隐私保护模型中不确定性数据的建模问题研究[J].计算机工程与科学,2011,33(9):7-12,6.基金项目
国家自然科学基金资助项目(61070032) （61070032）
