Towards K-Anonymous Non-Numerical Data via Semantic Resampling

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.

Provided by: Universitat Rostock Topic: Data Management Date Added: Jan 2013 Format: PDF

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