L–Diversity-Based Semantic Anonymaztion for Data Publishing

Abstract

Nowadays, publishing data publically is an important for many purposes especially for scientific research. Publishing this data in its raw form make it vulnerable to privacy attacks. Therefore, there is a need to apply suitable privacy preserving techniques on the published data. K-anonymity and L-diversity are well known techniques for data privacy preserving. These techniques cannot face the similarity attack on the data privacy because they did consider the semantic relation between the sensitive attributes of the data. In this paper, a semantic anonymization approach is proposed. This approach is based on the Domain based of semantic rules and the data owner rules to overcome the similarity attacks. The approach is enhanced privacy preserving techniques to prevent similarity attack and have been implemented and tested. The results shows that the semantic anonymization increase the privacy level and decreases the data utility.