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• W1601229331 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." @default.
• W1601229331 created "2016-06-24" @default.
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• W1601229331 date "2010-01-01" @default.
• W1601229331 modified "2023-09-25" @default.
• W1601229331 title "Anonymizing Categorical Data with a Recoding Method Based on Semantic Similarity" @default.
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• W1601229331 doi "https://doi.org/10.1007/978-3-642-14058-7_62" @default.
• W1601229331 hasPublicationYear "2010" @default.
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