A Dynamic Anonymization Privacy-Preserving Model Based on Hierarchical Sequential Three-Way Decisions

Data anonymization is one of the common techniques for ensuring data security and privacy. However, the existing anonymization techniques often suffer lower execution efficiency and unnecessary information loss when dealing with complex data. Therefore, we propose a dynamic anonymity privacy-preserving model based on hierarchical sequential three-way decisions. Specifically, we first divide the data into multiple granularity spaces by attributes and dynamically process the data in the granularity spaces. Then, in a single granularity space, we construct a generalization hierarchy for the data based on the attributes generalization trees and divide it into the positive, negative and boundary regions based on anonymous parameter. Next, we can acquire the positive and boundary regions by generalization and dynamically update the processed data at the next granularity. After that, we suppress the data in the final negative and boundary regions while releasing the positive region. To further improve data availability, we combine the idea of differential privacy by adding noise data to the final boundary region enabling its release and propose an enhanced anonymity model. Finally, we compare our proposed algorithms with other methods on six datasets. Experimental results show that our method effectively reduces processing costs, improves data usability and protects data privacy.