Towards K-Anonymous Non-Numerical Data via Semantic Resampling

Provided by: Universitat Rostock
Topic: Data Management
Format: PDF
Privacy should be carefully considered during the publication of data (e.g. database records) collected from individuals to avoid disclosing identities or revealing confidential information. Anonymisation methods aim at achieving a certain degree of privacy by performing transformations over non-anonymous data while minimising, as much as possible, the distortion (i.e. information loss) derived from these transformations. k-anonymity is a property typically considered when masking data, stating that each record (corresponding to an individual) is indistinguishable from at least k-1 other records in the anonymised dataset.

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