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Modeling and Storing the Uncertain Data in the k-anonymity Privacy Protection Model

Author: WuJiaWei
Tutor: LiuGuoHua
School: Donghua University
Course: Applied Computer Technology
Keywords: modeling uncertain data k-anonymity storage
CLC: TP309
Type: Master's thesis
Year: 2012
Downloads: 11
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Abstract

With the increased awareness of privacy protection, there is growing emphasis on published data privacy disclosure issues. For as little as possible so that private information was leaked, the researchers propose a variety of means of privacy protection.k-anonymity privacy protection model [1,2,3] which changes data into uncertainty has become a trusted privacy protection model for the publisher of data. However, constrained by the privacy protection requirements, even if the optimal k-anonymity algorithm can not produce completely accurate data,k-anonymity privacy protection model has led to the reduction of the data usability. The recipient rejects the data as junk data. To make the data recipient treat and manage data in k-anonymity privacy protection model expediently and increase data availability, the k-anonymous data is studied from another point of view this article, namely,k-anonymity privacy protection model is taken as a kind of uncertain data, then deal with it using uncertain ways.Modeling is the basis for 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 human with generalization, the probability that each instance after generalization is reduced to the original tuple is equal. Because of the specificity, the past modeling approaches of uncertainty data are not suitable for this kind of uncertainty data simply. In order to describe it, several new modeling methods are proposed in this paper:kattr model uses attribute-ors ways to describe the uncertainty of the quasi-identifier attribute values; ktuple model takes the quasi-identifier attribute values as nest relations and use tuple-ors ways to describe the relations; kupperlower model separates a quasi-identifier attribute to two fields:upper and lower; ktree model based on the property that k-anonymous table is a generalization result of the ordinary relation using generalization trees converts each quasi-identifier attribute into a tree reversely. The completeness and closure of these models are discussed later.Finally, the storage problems for models are studied,k-anonymity data is from the ordinary data using field generalization trees, they are independent each other, so the k-anonymous data attributes are also mutually independent and can be used to vertically split for storage. First, a unity relational model is gained from different models. Secondly, columns are used to store instances in uncertainty of each attribute value. At last, Multiple relational tables represents the uncertainty of k-anonymous data which are stored in a relational database

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