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Review on Privacy Preservation by Applying Scalable MapReduce BottomUp Generalization (MRBUG) Technique

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2016
Umar Y. Solanki, Rubeena A. Khan

Umar Y Solanki and Rubeena A Khan. Article: Review on Privacy Preservation by Applying Scalable MapReduce BottomUp Generalization (MRBUG) Technique. International Journal of Computer Applications 139(6):17-21, April 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

	author = {Umar Y. Solanki and Rubeena A. Khan},
	title = {Article: Review on Privacy Preservation by Applying Scalable MapReduce BottomUp Generalization (MRBUG) Technique},
	journal = {International Journal of Computer Applications},
	year = {2016},
	volume = {139},
	number = {6},
	pages = {17-21},
	month = {April},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}


Privacy is one of the most concerned issues in data publishing. Personal data like electronic health records and financial transaction records are usually deemed extremely sensitive although these data can offer significant human benefits if they are analyzed and mined by organizations such as disease research Centre. The emerging research field in data mining, Privacy Preserving Data Publishing (PPDP) [11], targets these challenges. The basic idea of PPDM is to modify the data in such a way so as to perform data mining algorithms effectively without compromising the security of sensitive information contained in the data. It aims at developing techniques that enable publishing data while minimizing data distortion for maintaining utility and ensuring that privacy is preserved.


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privacy, privacy preserving data mining(ppdm), k-anonymity, suppression, generalization ,mapreduce.