Abstract
Nowadays, more and more applications are dependent on storage and management of semi-structured information. For scientific research and knowledge-based decision-making, such data often needs to be published, e.g., medical data is released to implement a computer-assisted clinical decision support system. Since this data contains individuals’ privacy, they must be appropriately anonymized before to be released. However, the existing anonymization method based on l-diversity for hierarchical data may cause serious similarity attacks, and cannot protect data privacy very well. In this paper, we utilize fuzzy sets to divide levels for sensitive numerical and categorical attribute values uniformly (a categorical attribute value can be converted into a numerical attribute value according to its frequency of occurrences), and then transform the value levels to sensitivity levels. The privacy model ( α l e v h , k)-anonymity for hierarchical data with multi-level sensitivity is proposed. Furthermore, we design a privacy-preserving approach to achieve this privacy model. Experiment results demonstrate that our approach is obviously superior to existing anonymous approach in hierarchical data in terms of utility and security.
Highlights
Hospitals and other organizations often need to publish data, e.g., medical data or census data, for the purposes of scientific research and knowledge-based decision-making [1,2,3,4,5,6,7,8,9,10]
We propose a multi-level privacy-preserving approach in hierarchical data based on fuzzy sets
Q contains at least k hierarchical data records, and for every vertex v in the class representative of Q, the frequency of the values in vSA which belong to the sensitivity level i is less than or equal h [i ], where α h = {0.8, 0.6, 0.4, 0.2, 0.1}
Summary
Hospitals and other organizations often need to publish data, e.g., medical data or census data, for the purposes of scientific research and knowledge-based decision-making [1,2,3,4,5,6,7,8,9,10]. An equivalence class in an anonymous hierarchical dataset is a set of records with the same values for the QIs. the method does not consider the sensitivity of different sensitive attribute values, which lead to similarity attacks [15]. We propose a multi-level privacy-preserving approach in hierarchical data based on fuzzy sets. We utilize the fuzzy set theory to obtain the sensitivity levels for sensitive numerical and h , k)-anonymity for hierarchical data categorical attribute values, and present the privacy model This model can solve the similarity attack, and provide reasonable privacy protection for sensitive value in different sensitivity level. Experiment results demonstrate that our approach is superior to ClusTree in terms of utility and security
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