A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving

A State Decision Tree based Backtracking Algorithm for Multi-Sensitive Attribute Privacy Preserving

Yanchao Zhang, Qing Liu, JunJun Cheng, JiJia Yang
Copyright: © 2016 |Pages: 11
DOI: 10.4018/IJITN.2016040101
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Abstract

Beyond l-diversity model, an algorithm (l-BDT) based on state decision tree is proposed in this paper, which aims at protecting multi-sensitive attributes from being attacked. The algorithm considers the whole situations in equivalence partitioning for the first, prunes the decision tree according to some conditions for the second, and screens out the method with the least information loss of equivalence partitioning for the last. The analysis and experiments show that the l-BDT algorithm has the best performance in controlling the information loss. It can be ensured that the published data is the most closed towards the original data, so as to ensure that the published data is as useful as possible.
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2. Definition And Analysis Of Concerned Problems

Let us assume that a user will publish a relation T{A1,A2,…..,Ap,S1,S2,.....,Sd}.Ai(1≤i≤n) means quasi identity attribute, Sj(1≤j≤d), sensitive attribute. Let’s suppose that there are n records in T, meaning |T|=n, and every record will be ti(1≤j≤n). And t[X] indicating the X attribute of t.

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