A comprehensive review on privacy preserving data mining
Citation:
Aldeen Y.A.A.S, Salleh M, Razzaque M.A, A comprehensive review on privacy preserving data mining, SpringerPlus, 4, 1, 2015, 1 - 36Download Item:
Abstract:
Preservation of privacy in data mining has emerged as an absolute prerequisite for
exchanging confidential information in terms of data analysis, validation, and publish-
ing. Ever-escalating internet phishing posed severe threat on widespread propagation
of sensitive information over the web. Conversely, the dubious feelings and conten-
tions mediated unwillingness of various information providers towards the reliability
protection of data from disclosure often results utter rejection in data sharing or
incorrect information sharing. This article provides a panoramic overview on new per
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spective and systematic interpretation of a list published literatures via their meticu-
lous organization in subcategories. The fundamental notions of the existing privacy
preserving data mining methods, their merits, and shortcomings are presented. The
current privacy preserving data mining techniques are classified based on distortion,
association rule, hide association rule, taxonomy, clustering, associative classification,
outsourced data mining, distributed, and k-anonymity, where their notable advantages
and disadvantages are emphasized. This careful scrutiny reveals the past development,
present research challenges, future trends, the gaps and weaknesses. Further signifi-
cant enhancements for more robust privacy protection and preservation are affirmed
to be mandatory.
Author's Homepage:
http://people.tcd.ie/razzaqum
Author: RAZZAQUE, MOHAMMAD
Type of material:
Journal ArticleCollections
Series/Report no:
SpringerPlus4
1
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Full text availableKeywords:
Privacy preservingDOI:
http://dx.doi.org/10.1186/s40064-015-1481-xMetadata
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