Abstract
Moustakides & Verykios in [50, 51] propose two border based methodologies that rely on the max–min criterion for the hiding of the sensitive itemsets. Both methodologies use the revised positive border of the frequent itemsets to track the impact of each tentative item modification that helps towards the hiding of a sensitive itemset. Then, they select to apply those item modifications to the original database that effectively conceal all the sensitive knowledge, while minimally affecting the itemsets of the revised positive border and, consequently, the nonsensitive frequent itemsets. For each item of a sensitive itemset, the Max–Min algorithms identify the set of itemsets from the revised positive border which depend on it, and select among them the ones that are supported the least. Then, from among all minimum supported border itemsets (coming from the previously computed sets for the different items of the sensitive itemset), the itemset with the highest support is selected as it is the one with the maximum distance from the borderline that separates the frequent from the infrequent itemsets.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2010 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Gkoulalas-Divanis, A., Verykios, V.S. (2010). Max-Min Algorithms. In: Association Rule Hiding for Data Mining. Advances in Database Systems, vol 41. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-6569-1_11
Download citation
DOI: https://doi.org/10.1007/978-1-4419-6569-1_11
Published:
Publisher Name: Springer, Boston, MA
Print ISBN: 978-1-4419-6568-4
Online ISBN: 978-1-4419-6569-1
eBook Packages: Computer ScienceComputer Science (R0)