A Study on Personalized Privacy Preservation Framework based Ontology Document

Abstract

This paper considers data-publishing, where the publisher needs to specify sensitive information that should be protected. If a document that contains such information is published carelessly, users could infer unauthorized information by exploiting common sense inference. In this paper, we propose a framework that uses encryption for preventing sensitive information from being exposed to unauthorized users. In this framework, sensitive data contained in ontology documents are encrypted separately, and then all encrypted data are moved from their original document to the protected information set and bundled with and encrypted structure index. Our experiments show that the propose framework prevents information leakage via data inference. Moreover, the experiment results show that our method demonstrates better query processing performance than the existing method.