k-anonymity: a model for protecting privacy
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems
Transforming data to satisfy privacy constraints
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Verbs semantics and lexical selection
ACL '94 Proceedings of the 32nd annual meeting on Association for Computational Linguistics
Data Privacy through Optimal k-Anonymization
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Mondrian Multidimensional K-Anonymity
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Utility-based anonymization for privacy preservation with less information loss
ACM SIGKDD Explorations Newsletter
Towards optimal k-anonymization
Data & Knowledge Engineering
Privacy-Preserving Data Mining: Models and Algorithms
Privacy-Preserving Data Mining: Models and Algorithms
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
Privacy Preserving Categorical Data Analysis with Unknown Distortion Parameters
Transactions on Data Privacy
Ontology-based anonymization of categorical values
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part I
Ontology-based anonymization of categorical values
MDAI'10 Proceedings of the 7th international conference on Modeling decisions for artificial intelligence
Journal of Biomedical Informatics
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The exploitation of sensible data associated to individuals requires a proper anonymization in order to preserve the privacy. Even though several masking methods have been designed for numerical data, very few of them deal with textual information. During the masking process, information loss should be minimized in order to enable a proper analysis of data with data mining methods. In the case of textual data, the quality of the anonymized dataset is closely related to the preservation of semantics, a dimension which has been only shallowly considered in some previous works, by using small and ad-hoc hierarchies of words. In this work we want to study the use of large and standard ontologies as the base to perform the anonymization of textual variables. We will evaluate the role of ontologies in preserving the utility of the anonymized information when a partition of the objects is done with unsupervised clustering methods. Results show that by exploiting detailed ontologies, one is able to improve the preservation of the data semantics in comparison to approaches based on ad-hoc structures and data distribution metrics.