Abstract
In this chapter, we review some popular support-based and confidence-based distortion algorithms that have been proposed in the association rule hiding literature. Distortion-based approaches operate by selecting specific items to include to (or exclude from) selected transactions of the original database in order to facilitate the hiding of the sensitive association rules. Two of the most commonly employed strategies for data distortion involve the swapping of values between transactions [10, 20], as well as the deletion of specific items from the database [54]. In the rest of this chapter, we present an overview of these approaches along with other methodologies that also fit in the same class.
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
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). Distortion Schemes. 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_6
Download citation
DOI: https://doi.org/10.1007/978-1-4419-6569-1_6
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)