TITLE:
Utility-Based Anonymization Using Generalization Boundaries to Protect Sensitive Attributes
AUTHORS:
Abou-el-ela Abdou Hussien, Nagy Ramadan Darwish, Hesham A. Hefny
KEYWORDS:
Privacy, Privacy Preserving Data Mining, K-Anonymity, Generalization Boundaries, Suppression
JOURNAL NAME:
Journal of Information Security,
Vol.6 No.3,
June
15,
2015
ABSTRACT: Privacy preserving data mining (PPDM) has become more and more important
because it allows sharing of privacy sensitive data for analytical purposes. A big
number of privacy techniques were developed most of which used the k-anonymity
property which have many shortcomings, so other privacy techniques were introduced
(l-diversity, p-sensitive k-anonymity, (α, k)-anonymity, t-closeness, etc.). While they are different in their methods and quality
of their results, they all focus first on masking the data, and then protecting
the quality of the data. This paper is concerned with providing an enhanced privacy
technique that combines some anonymity techniques to maintain both privacy and data
utility by considering the sensitivity values of attributes in queries using sensitivity
weights which determine taking in account utility-based anonymization and then only
queries having sensitive attributes whose values exceed threshold are to be changed
using generalization boundaries. The threshold value is calculated depending on
the different weights assigned to individual attributes which take into account
the utility of each attribute and those particular attributes whose total
weights exceed the threshold values is changed using generalization boundaries and
the other queries can be directly published. Experiment results using UT dallas
anonymization toolbox on real data set adult database from the UC machine learning
repository show that although the proposed technique preserves privacy, it also
can maintain the utility of the publishing data.