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A Novel Privacy Preserving Association Rule Mining using Hadoop

Authors:
Kangsoo Jung
Sehwa Park
Sungyong Cho
Seog Park

Keywords: Privacy preserving data mining; Association rule mining; Hadoop.

Abstract:
Hadoop is a popular open source distributed system that can processes large scale data. Meanwhile, data mining is one of the techniques used to find pattern and gain knowledge from data sets, as well as improve massive data processing utility when combined with the Hadoop framework. However, data mining constitutes a possible threat to privacy. Although numerous studies have been conducted to address this problem, such studies were insufficient and had several drawbacks such as privacy-data utility trade-off. In this paper, we focus on privacy preserving data mining algorithm technique, particularly the association rule mining algorithm, which is a representative data mining algorithm. We propose a novel privacy preserving association rule mining algorithm in Hadoop that prevents privacy violation without the loss of data utility. Through the experimental results, the proposed technique is validated to prevent the exposure of sensitive data without degradation of data utilization.

Pages: 131 to 137

Copyright: Copyright (c) IARIA, 2014

Publication date: August 24, 2014

Published in: conference

ISSN: 2308-4464

ISBN: 978-1-61208-358-2

Location: Rome, Italy

Dates: from August 24, 2014 to August 28, 2013