计算机技术与发展2017，Vol.27Issue(7)：111-114,119,5.DOI:10.3969/j.issn.1673-629X.2017.07.026
一种基于决策树的隐私保护数据流分类算法
A Decision Tree-based Privacy Preserving Classification Mining Algorithm for Data Streams
摘要
Abstract
Privacy preserving data mining methods are mainly based on perturbation and randomization approaches and secure multi-party computation approaches.Due to the high-speed data streams with unlimited continuous and dynamic characteristics,these methods are still inadequate.In order to solve privacy leaking problem on current data streams mining application,a privacy preserving fast decision tree mining algorithm for data streams named as PPFDT has been designed and implemented.It adds random noises to protect data privacy and improves the data mining algorithm named VFDT,and uses threshold method to find the best split attribute and the best split point of perturbed data streams,so that a decision tree is directly built on perturbed data streams.Then the decision tree is used to classify original data streams and perturbed data streams for getting accurate results.From the aspects of the privacy protection degree of the PPFDT algorithm and the classification performance on the direct perturbed data stream,the algorithm has been experimentally verified on the Waveform dataset of UCI.The experimental results show that the algorithm can achieve certain degrees of privacy protection,and at the same time,classify data streams fast and accurately.关键词
决策树/隐私保护/数据流/分类Key words
decision tree/privacy preserving/data stream/classification分类
信息技术与安全科学引用本文复制引用
陈煜,李玲娟..一种基于决策树的隐私保护数据流分类算法[J].计算机技术与发展,2017,27(7):111-114,119,5.基金项目
国家自然科学基金资助项目(61302158,61571238) （61302158,61571238）
