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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Xiong, P., Zhu, T., Jin, D.: Differential private data publishing algorithm for building decision tree. Appl. Res. Comput. 31(10), 3108–3112 (2014)
Sun, Z., Sun, F.: Constructing the privacy protection system of users’ big data based on institutional trust. Res. Libr. Sci. 436(17), 98–100 (2018)
Huang, R., Wen, F.: The construction of policy problems of opening and sharing government data in China. Libr. Inf. Serv. 61(20), 26–36 (2017)
Zhu, D., Li, X.B., Wu, S.: Identity disclosure protection: a data reconstruction approach for privacy-preserving data mining. Decis. Support Syst. 48(1), 133–140 (2019)
Kasai, H., Uchida, W., Kurakake, S.: A service provisioning system for distributed personalization with private data protection. J. Syst. Softw. 80(12), 2025–2038 (2017)
Kumar, S., Mahulikar, S.P.: Design of thermal protection system for reusable hypersonic vehicle using inverse approach. J. Spacecraft Rock. 54(2), 436–446 (2017)
Chern, J., Liu, C.: Morakot post-disaster reconstruction management using public and private resources for disaster prevention and relief efforts. J. Chinese Inst. Eng. 37(5), 621–634 (2014)
Bai, C., Lu, J., Tao, Z.: Property rights protection and access to bank loans: evidence from private enterprises in China. Econ. Trans. 14(4), 611–628 (2016)
Chang, L., Moskowitz, I.S.: An integrated framework for database privacy protection. In: Thuraisingham, B., van de Riet, R., Dittrich, K.R., Tari, Z. (eds.) Data and Application Security. IFIP International Federation for Information Processing, vol 73. Springer, Boston (2002). https://doi.org/10.1007/0-306-47008-X_15
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).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-981-16-3150-4_30
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-3149-8
Online ISBN: 978-981-16-3150-4
eBook Packages: Computer ScienceComputer Science (R0)