Adaptive Utility-based Anonymization Model: Performance Evaluation on Big Data Sets

被引：12

作者：

Panackal, Jisha Jose ^{[1
]}

Pillai, Anitha S. ^{[1
]}

机构：

[1] Hindustan Univ, Sch Comp Sci, Madras 603103, Tamil Nadu, India

来源：

BIG DATA, CLOUD AND COMPUTING CHALLENGES | 2015年 / 50卷

关键词：

Anonymization; Classification; Data Mining; Privacy;

D O I：

10.1016/j.procs.2015.04.037

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Data Anonymization is one of the globally accepted mechanisms for the protection of privacy of individuals in data publishing scenario. Normally the data anonymization impacts on the quality of data especially critical to the success of knowledge-based applications. An intelligent approach based on association mining namely, Adaptive Utility-based Anonymization (AUA) has been proposed in order to deal with this issue. Initially the model is tested with sample instances of original data set National Family Health Survey (NFHS-3) and this paper includes performance evaluation of AUA model using data sets and proves that the data anonymization can be done without compromising the quality of data mining results. (C) 2015 The Authors. Published by Elsevier B.V.

引用

页码：347 / 352

页数：6

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