A Modified KACTUS Algorithm Based Multi-Dimensional Suppression for K-Anonymity
Main Article Content
Abstract
Data mining is the process of extracting hidden information from database. The current trend in business collaboration shares the data and mined results to gain mutual benefit. The problem of privacy-preserving data mining has become more important in recent years because of the increasing ability to store personal data about users, and the increasing sophistication of data mining algorithms to leverage this information. Two common manipulation techniques used to achieve k-anonymity of a dataset are generalization and suppression. K-Anonymity of Classification Trees Using Suppression (kACTUS) is observed to provide good results in achieving k-anonymity. In KACTUS efficient multidimensional suppression is performed, that is values are suppressed only on certain records depending on other attribute values, without the need for manually-produced domain hierarchy trees. The k-anonymity models is extended by providing new definitions and use several anonymization techniques together in order to get better results in terms of accuracy than reported in the literature.
Keywords: KACTUS 2; k-anonymity; privacy preserving; decision tree; computational complexity
Downloads
Article Details
COPYRIGHT
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.