**6. Conclusions**

Hierarchical data has become ubiquitous with the advent of document-oriented databases and the wide use of markup languages. However, this data contains privacy information, and so must be appropriately anonymized before it is to be published for scientific research and decision-making. To prevent similarity attacks in hierarchical data, in this paper, we use fuzzy set theory to partition sensitive values for a sensitive numerical or categorical attribute uniformly into five levels by converting the categorical attribute values into the numerical attribute values, and then map the five value levels to five sensitivity levels. According to these sensitivity levels, we propose privacy model (*αhlev*, *k*)-anonymity for hierarchical data with multi-level sensitivity and design a privacy-preserving approach to achieve (*αhlev*, *k*)-anonymity. Experimental results show that the average dissimilarity degree of these equivalence classes in anonymized hierarchical data obtained by our approach is higher than that for existing anonymous approaches in hierarchical data. Thus, our approach can effectively resist similarity attacks. Also, our approach causes less information loss and so improves the utility of anonymized hierarchical data.

**Author Contributions:** J.W. (Jinyan Wang), G.C. and X.L. put forward privacy model and the anonymization method, G.C. implemented the anonymization method with Python, J.W. (Jinyan Wang) and X.L. wrote the original manuscript and C.L. and J.W. (Jingli Wu) improved the writing.

**Funding:** This paper was supported by the National Natural Science Foundation of China (Nos. 61502111, 61763003, 61672176, 61762015, 61562007, 61662008), Guangxi Natural Science Foundation (Nos. 2016GXNSFAA380192, 2015GXNSFBA139246), Guangxi "Bagui Scholar" Teams for Innovation and Research Project, Guangxi Special Project of Science and Technology Base and Talents (AD16380008), and Guangxi Collaborative Innovation Center of Multisource Information Integration and Intelligent Processing.

**Conflicts of Interest:** The authors declare no conflict of interest.
