Sensitivity Based Data Anonymization Model with Mixed Generalization
Esther Gachanga, Michael Waema Kimwele, Lawrence Nderu · International Journal of Advances in Scientific Research and Engineering · 2019
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. Publisheddatasets contain sensitive information. Different sensitive attributes may have different levels of sensitivity. This study presents amodel where the anonymization of tuples is based on the level of sensitivity of the sensory attributes. The study groups sensitiveattributes 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 theanonymized dataset. The results from the experiments show that by using the sensitivity based approach to anonymization, thequality of anonymized datasets can be preserved