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A comprehensive review on privacy preserving data mining
#MMPMID26587362
Aldeen YA
; Salleh M
; Razzaque MA
Springerplus
2015[]; 4
(?): 694
PMID26587362
show ga
Preservation of privacy in data mining has emerged as an absolute prerequisite
for exchanging confidential information in terms of data analysis, validation,
and publishing. Ever-escalating internet phishing posed severe threat on
widespread propagation of sensitive information over the web. Conversely, the
dubious feelings and contentions 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 perspective and systematic
interpretation of a list published literatures via their meticulous 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 significant enhancements for more robust privacy protection
and preservation are affirmed to be mandatory.
free
free
free
