Abstract
With the arrival of the information age, privacy leaks have been getting increasingly serious. The commonly-used method for privacy protection is to introduce k-anonymity when data publishing. In this paper, how to employ k-anonymity in stream data sets is studied, and the relationship and the sensitivity matrix of the basic knowledge about Quasi identifier (QI) and sensitive attributes are established. RSLK-anonymity algorithm is proposed for solving the private information leakage during the streaming data publishing. The main idea is to anonymize the streaming data set on the basis of sliding window as well as relation and sensitive matrix, which can make anonymous streaming data effectively defense background knowledge attack and homogeneity attack while solving sensitive attribute diversity. Experimental results indicate that RSLK-anonymity algorithm is practical, effective and highly efficient.
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Acknowledgement
This paper is supported by the science and technology project of State Grid Corporation of China: “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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Jia, Y., Tao, P., Zhou, D., Li, B. (2021). Incremental Anonymous Privacy-Protecting Data Mining Method Based on Feature Correlation Algorithm. 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_18
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DOI: https://doi.org/10.1007/978-981-16-3150-4_18
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