Sensitivity Based Data Anonymization Model with Mixed Generalization

Abstract

Published micro-data may contain sensitive information about individuals which should not be revealed. Anonymization approaches have been considered a possible solution to the challenge of preserving privacy while publishing data. Published
datasets contain sensitive information. Different sensitive attributes may have different levels of sensitivity. This study presents a
model where the anonymization of tuples is based on the level of sensitivity of the sensory attributes. The study groups sensitive
attributes into highly sensitive and non-sensitive attributes. Tuples with non-sensitive attributes are anonymized. The study conducts experiments with real-life datasets and uses naïve Bayes, C4.5 and simple logistic classifiers to assess the quality of the
anonymized dataset. The results from the experiments show that by using the sensitivity based approach to anonymization, the
quality of anonymized datasets can be preserved