Nowadays, more and more applications are dependent on storage and management of\nsemi-structured information. For scientific research and knowledge-based decision-making, such data\noften needs to be published, e.g., medical data is released to implement a computer-assisted clinical\ndecision support system. Since this data contains individualsâ?? privacy, they must be appropriately\nanonymized before to be released. However, the existing anonymization method based on l-diversity\nfor hierarchical data may cause serious similarity attacks, and cannot protect data privacy very well.\nIn this paper, we utilize fuzzy sets to divide levels for sensitive numerical and categorical attribute\nvalues uniformly (a categorical attribute value can be converted into a numerical attribute value\naccording to its frequency of occurrences), and then transform the value levels to sensitivity levels.\nThe privacy model (....)-anonymity for hierarchical data with multi-level sensitivity is proposed.\nFurthermore, we design a privacy-preserving approach to achieve this privacy model. Experiment\nresults demonstrate that our approach is obviously superior to existing anonymous approach in\nhierarchical data in terms of utility and security.
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