Abstract
With the enormous growth of the Information Society and the necessity to enable access and exploitation of large amounts of data, the preservation of its confidentiality has become a crucial issue. Many methods have been developed to ensure the privacy of numerical data but very few of them deal with textual (categorical) information. In this paper a new method for protecting the individual’s privacy for categorical attributes is proposed. It is a masking method based on the recoding of words that can be linked to less than k individuals. This assures the fulfillment of the k-anonymity property, in order to prevent the re-identification of individuals. On the contrary to related works, which lack a proper semantic interpretation of text, the recoding exploits an input ontology in order to estimate the semantic similarity between words and minimize the information loss.
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Martínez, S., Valls, A., Sánchez, D. (2010). Anonymizing Categorical Data with a Recoding Method Based on Semantic Similarity. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. Applications. IPMU 2010. Communications in Computer and Information Science, vol 81. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14058-7_62
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DOI: https://doi.org/10.1007/978-3-642-14058-7_62
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