International Journal of Advances in Scientific Research and Engineering-IJASRE

Sensitivity-Based Data Anonymization Model with Mixed Generalization

Article Category: Computer Science and Information Engineering

DOI: 10.31695/IJASRE.2019.33150

Pages: 66-72

Author: Esther Gachanga,Michael Kimwele,Lawrence Nderu

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.

Keyword: Sensitive, Anonymity, Privacy, Classification, Data Publishing

This work is licensed under a Creative Commons Attribution 4.0 (International) Licence. (CC BY-NC 4.0)
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