ODTT: Optimized Dynamic Taxonomy Tree with Differential Privacy
Yijie Gui, Qizhi Chen, Wensheng Gan, Yongdong Wu · 2023
For cybersecurity, privacy protection in big data has received more and more attention and research. Differential privacy is one of the important privacy protection methods, and our work pays attention to differential privacy based on the dynamic taxonomy tree, which can protect the publishing of set-valued data effectively. We propose the optimized dynamic taxonomy tree (ODTT) algorithm as a better and more general way to protect privacy in set-valued datasets. It makes better use of data and reduces noise compared to other privacy-preserving algorithms that use taxonomy tree partitioning. The previous algorithm did not make full use of the characteristics of the dataset when constructing the taxonomy tree, so a 2-itemset’s matrix is used in the proposed algorithm to increase the pseudoempty nodes and reduce the addition of noise. More importantly, we apply the consistency constraint method to the construction of the ODTT algorithm. This retains more statistical characteristics of the original dataset by constraining the noise counts in the leaf partitions of the partition tree. Furthermore, ODTT is extended to deal with dynamic datasets. Finally, we compare the proposed ODTT algorithm with the state-of-the-art CDTT algorithm, by performing a series of experiments and using some evaluation metrics. Experimental results show that ODTT is more general and has higher usability while satisfying the security of differential privacy.