@book{JRC43963,
	editor = {},
	address = {},
	year = {2008},
	author = {Nai Fovino I and Trombetta A},
	isbn = {},
	abstract = {Privacy is one of the most important properties an informa-
		tion system must satisfy. A relatively new trend shows that classical ac-
		cess control techniques are not sufficient to guarantee privacy when data-
		mining techniques are used. Privacy Preserving Data Mining (PPDM)
		algorithms have been recently introduced with the aim of sanitizing the
		database in such a way to prevent the discovery of sensible information
		(e.g. association rules). A drawback of such algorithms is that the intro-
		duced sanitization may disrupt the quality of data itself. In this paper
		we introduce a new methodology and algorithms for performing useful
		PPDM operations, while preserving the data quality of the underlying
		database.
		},
	title = {Information Driven Association Rule Hiding Algorithms},
	url = {http://www.ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=4621664&isnumber=4621577},
	volume = {},
	number = {},
	journal = {},
	pages = {383-387},
	issn = {},
	publisher = {IEEE},
	doi = {}
}