Inverted XML Access Control Model Based on Ontology Semantic Dependency

In the era of big data, the conflict between data mining and data privacy protection is increasing day by day. Traditional information security focuses on protecting the security of attribute values without semantic association. The data privacy of big data is mainly reflected in the effective use of data without exposing the user's sensitive information. Considering the semantic association, reasonable security access for privacy protect is required. Semi-structured and self-descriptive XML (eXtensible Markup Language) has become a common form of data organization for database management in big data environments. Based on the semantic integration nature of XML data, this paper proposes a data access control model for individual users. Through the semantic dependency between data and the integration process from bottom to top, the global visual range of inverted XML structure is realized. Experimental results show that the model effectively protects the privacy and has high access efficiency.