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A Survey on Privacy Preserving Decision Tree Classifier

机译:隐私保护决策树分类器研究

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In recent year's privacy preservation in data mining has become an important issue. A new class of data mining method called privacy preserving data mining algorithm has been developed. The aim of these algorithms is protecting the sensitive information in data while extracting knowledge from large amount of data. The extracted knowledge is generally expressed in the form of cluster, decision tree or association rule allow one to mine the information. Several data modification technique like randomization method, anonymization method, distributed privacy technique have been developed to incorporating privacy mechanism and allow to hide sensitive pattern or itemset before data mining process is executed. This paper mainly focuses on general classification technique decision tree classifier for preserving privacy. It presents a survey on decision tree learning on various privacy techniques
机译:近年来,数据挖掘中的隐私保护已成为一个重要问题。开发了一种新的数据挖掘方法,称为隐私保护数据挖掘算法。这些算法的目的是保护数据中的敏感信息,同时从大量数据中提取知识。提取的知识通常以聚类,决策树或关联规则的形式表示,允许人们挖掘信息。已经开发了多种数据修改技术,例如随机化方法,匿名化方法,分布式隐私技术,以结合隐私机制,并允许在执行数据挖掘过程之前隐藏敏感模式或项目集。本文主要关注用于保护隐私的通用分类技术决策树分类器。它提供了有关各种隐私技术的决策树学习的调查

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