Privacy Preserving Data Mining: A Noise Addition Framework Using a Novel Clustering Technique

Md Zahidul Islam, Ljiljana Brankovic

Research output: Contribution to journalArticle

64 Citations (Scopus)
60 Downloads (Pure)

Abstract

During the whole process of data mining (from data collection to knowledge discovery) various sensitivedata get exposed to several parties including data collectors, cleaners, preprocessors, miners and decisionmakers. The exposure of sensitive data can potentially lead to breach of individual privacy. Therefore,many privacy preserving techniques have been proposed recently. In this paper we present a framework that uses a few novel noise addition techniques for protecting individual privacy while maintaining ahigh data quality. We add noise to all attributes, both numerical and categorical. We present a novel technique for clustering categorical values and use it for noise addition purpose. A security analysis is also presented for measuring the security level of a data set.
Original languageEnglish
Pages (from-to)1214-1223
Number of pages10
JournalKnowledge-Based Systems
Volume24
Issue number8
DOIs
Publication statusPublished - Dec 2011

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