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
Swoogle: a search and metadata engine for the semantic web
Proceedings of the thirteenth ACM international conference on Information and knowledge management
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
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Ontology Learning and Population from Text: Algorithms, Evaluation and Applications
Utility-based anonymization for privacy preservation with less information loss
ACM SIGKDD Explorations Newsletter
Towards optimal k-anonymization
Data & Knowledge Engineering
Privacy-preserving anonymization of set-valued data
Proceedings of the VLDB Endowment
Anonymization of set-valued data via top-down, local generalization
Proceedings of the VLDB Endowment
The Role of Ontologies in the Anonymization of Textual Variables
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
The Role of Ontologies in the Anonymization of Textual Variables
Proceedings of the 2010 conference on Artificial Intelligence Research and Development: Proceedings of the 13th International Conference of the Catalan Association for Artificial Intelligence
Towards knowledge intensive data privacy
DPM'10/SETOP'10 Proceedings of the 5th international Workshop on data privacy management, and 3rd international conference on Autonomous spontaneous security
On the declassification of confidential documents
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
Semantically-grounded construction of centroids for datasets with textual attributes
Knowledge-Based Systems
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The analysis of sensible data requires a proper anonymization of values in order to preserve the privacy of individuals. Information loss should be minimized during the masking process in order to enable a proper exploitation of data. Even though several masking methods have been designed for numerical data, very few of them deal with categorical (textual) information. In this case, the quality of the anonymized dataset is closely related to the preservation of semantics, a dimension which is commonly neglected of shallowly considered in related words. In this paper, a new masking method for unbounded categorical attributes is proposed. It relies on the knowledge modeled in ontologies in order to semantically interpret the input data and perform data transformations aiming to minimize the loss of semantic content. On the contrary to exhaustive methods based on simple hierarchical structures, our approach relies on a set of heuristics in order to guide and optimize the masking process, ensuring its scalability when dealing with big and heterogenous datasets and wide ontologies. The evaluation performed over real textual data suggests that our method is able to produce anonymized datasets which significantly preserve data semantics in comparison to apporaches based on data distribution metrics.