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In this paper, for the lack of attribute sensitivity considerations of existing k-anonymous schemes and how to minimize the amount of information loss, we propose a hierarchical DP-K anonymous data release model based on binary tree clustering. The division of similar data records into the same equivalent class by the binary tree-based clustering algorithm BTCA can improve the effect of clustering, reduce the information loss caused by anonymous data set release, and improve the data availability. The clustered anonymous data sets are reallocated to different privacy budgets according to the privacy rights of the QI attribute, and the hierarchical protection of the data with different degrees of sensitivity can be realized through the differential privacy noise increase mechanism, which enhances the privacy of the data. In the end, the proposed algorithm can effectively reduce the information loss and insufficient privacy protection in the generation process of anonymous data set. However, in this paper, only a single sensitive attribute in the data set is selected. For the data set of high-dimensional sensitive attribute is included, the efficiency and availability of the algorithm may be insufficient, which is also the place to be improved in the future. And, the biggest challenge of fusion difference privacy mechanism is the balance of privacy and availability, how to keep the result data privacy at the same time to reduce noise, based on the tradeoff between utility and privacy to adjust data privacy methods, and create metrics to evaluate the quality of measuring model information loss and privacy protection, is a challenge for future research.
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IEEE/ICACT20230212 Slide.17
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