Abstract
With the growth of information explosion, the trend of data sharing and information exchange is gradually becoming obvious, and more and more attention has been paid to privacy. One of the main objectives of database privacy protection research is to protect sensitive information stored in databases from inference by ordinary database-users. In this paper, a framework based on Oracle database is proposed to aid the formal analysis of the database inference problem. The framework is based on association networks, consisting of similarity metrics and Bayesian network models (BNM), and aims to address the database privacy protection problem. First, the similarity analysis of the data is used to distinguish and check similar attributes. Second, the probability dependence of attributes is analyzed. Blocking and aggregation are used to prevent the association of data, and the associated network is used for analysis. The results show that the constructed database privacy protection system can be realized in many aspects such as scope reduction, blocking, aggregation and so on, and can finally ensure the effective protection of database privacy.
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Acknowledgement
This thesis is derived from the National Grid Corporation’s Science and Technology Tackle Project. “Research and Application of Key Technology of Data Sharing and Distribution Security for Data Center” (Grand No. 5700-202090192A-0-0-00).
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Liu, S., Zhu, H., Zhao, T., Wang, H., Gao, X., Yang, R. (2021). Oracle Data Privacy Protection System of Virtual Database. In: Tian, Y., Ma, T., Khan, M.K. (eds) Big Data and Security. ICBDS 2020. Communications in Computer and Information Science, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-3150-4_30
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DOI: https://doi.org/10.1007/978-981-16-3150-4_30
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