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A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Tasneem Jahan, Amit Saxena

Tasneem Jahan and Amit Saxena. A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern. International Journal of Computer Applications 182(5):7-11, July 2018. BibTeX

	author = {Tasneem Jahan and Amit Saxena},
	title = {A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern},
	journal = {International Journal of Computer Applications},
	issue_date = {July 2018},
	volume = {182},
	number = {5},
	month = {Jul},
	year = {2018},
	issn = {0975-8887},
	pages = {7-11},
	numpages = {5},
	url = {},
	doi = {10.5120/ijca2018917438},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Current scenario of large databases is in point of fact a major issue. Although, the conventional information examination seems to deal the extensive amounts of information. But the data analysts also attempt to analyze the productivity of data. This proposed work is an attempt to resolve the issue of digital information security by finding the highly frequent items in the dataset. Modified Frequent Web Access Pattern algorithm was developed in this work which find patterns in two scans. Technique called as super class substitution will be used here for perturbation of sensitive set of rules. It offers an added advantage of reducing the risk and the utility of database is also increased. Our experiment is carried out on a genuine dataset. The outcomes here, have shown that proposed work has better results over the previous methodologies.


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Data Mining, PPDM, MFWAP, Super class substitution, Data Perturbation,