Modeling the Uncertain Data in the KAnonymity Privacy Protection Model

Abstract

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 original 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identifier attribute values of the kanonymity privacy protection model; the Ktuple model takes the quasiidentifier attribute values as relations and use the tupleors ways to describe the relations; the Kupperlower model separates some generalization values to two fields: the upper limit and the lower limit; the Ktree 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.

Key words: modeling;uncertain data;kanonymity;model space;completeness;closure

WU Jiawei,LIU Guohua,WANG Mei. Modeling the Uncertain Data in the KAnonymity Privacy Protection Model[J]. J4, 2011, 33(9): 7-12.

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