A semantic-based inference control algorithm for OWL repository privacy protection

• Published in: Computer networks (Amsterdam, Netherlands : 1999) Vol. 156; pp. 1 - 8
• Main Authors: Qi, Yuying, Yao, Xuanxia, Zhu, Tao, Ning, Huansheng
• Format: Journal Article
• Language: English
• Published: Amsterdam Elsevier B.V 19.06.2019; Elsevier Sequoia S.A
• Subjects: Access control; Algorithms; Control algorithms; Control theory; Inference; Inference rules; Knowledge representation; OWL Repository; Programming languages; Semantic value; Semantic web; Semantics; Sensitive triples; Web Ontology Language-OWL; OWL Repository; Sensitive triples; Inference rules; Semantic value
• ISSN: 1389-1286; 1872-7069
• DOI: 10.1016/j.comnet.2019.03.017

Web Ontology Language (OWL) designed to represent the class and relationships between classes is bringing a technological revolution in the Semantic Web. More and more knowledge is stored in the form of OWL. OWL repository refers to the systems that can store, query and infer OWL data. In OWL repository, information that users are forbidden to access (sensitive triples) can be inferred from information that the user have access to (non-sensitive triples) through inference rules, which brings about a security problem that some users’ sensitive information may be leaked. To protect sensitive triples, some researches have been done on the security problem, which mainly focus on removing some non-sensitive triples with low access frequency to achieve inference control. However, access frequency of a triple is unstable and it can not fully represent the importance of semantic information. In this paper, an inference control algorithm based on Semantic Value and access frequency is proposed. We introduced the concept of Degree of triples and Semantic Value, which can fully represent the importance of the semantic information represented by the triple. Moreover, we did experiments from the perspective of protecting sensitive triples of every user. Experimental results show that this algorithm can effectively prevent illegal inference and minimize the loss of semantic information.