Association Rule Hiding for Data Mining

• Main Authors: Gkoulalas-Divanis, Aris. (Author), Verykios, Vassilios S. (Author)
• Corporate Author: SpringerLink (Online service)
• Format: Electronic
• Language: English
• Published: Boston, MA : Springer US, 2010.
• Series: Advances in Database Systems, 41
• Subjects: Computer science.; Operating systems (Computers).; Data structures (Computer science).; Computer software.; Database management.; Information systems.; Artificial intelligence.; Computer Science.; Database Management.; Information Systems Applications (incl.Internet).; Artificial Intelligence (incl. Robotics).; Data Structures, Cryptology and Information Theory.; Algorithm Analysis and Problem Complexity.; Performance and Reliability.
• Online Access: https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4419-6569-1

Privacy and security risks arising from the application of different data mining techniques to large institutional data repositories have been solely investigated by a new research domain, the so-called privacy preserving data mining. Association rule hiding is a new technique on data mining, which...

Table of Contents:

• Part I Fundamental Concepts
• Introduction
• Background
• Classes of Association Rule Hiding Methodologies
• Other Knowledge Hiding Methodologies
• Part II Heuristic Approaches
• Distortion Schemes
• Blocking Schemes
• Part III Border Based Approaches
• Border Revision for Knowledge Hiding
• BBA Algorithm
• MaxMin Algorithms
• Part IV Exact Hiding Approaches
• Menons Algorithm
• Inline Algorithm
• TwoPhase Iterative Algorithm
• Hybrid Algorithm
• Parallelization Framework for Exact Hiding
• Quantifying the Privacy of Exact Hiding Algorithms
• Part V Epilogue
• Conclusions
• Roadmap to Future Work
• References
• Index.