K-anonymity: A note on the trade-off between data utility and data security – Ahnaf Al Rafi

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Researchers often use data from multiple datasets to conduct credible econometric and statistical analysis. The most reliable way to link entries across such datasets is to exploit unique identifiers if those are available. Such linkage however may result in privacy violations revealing sensitive information about some individuals in a sample. Thus, a data curator with concerns for individual privacy may choose to remove certain individual information from the private dataset they plan on releasing to researchers. The extent of individual information the data curator keeps in the private dataset can still allow a researcher to link the datasets, most likely with some errors, and usually results in a researcher having several feasible combined datasets. One conceptual framework a data curator may rely on is k-anonymity, k ? 2, which gained wide popularity in computer science and statistical community. To ensure k-anonymity, the data curator releases only the amount of identifying inform

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  • Язык книги: русский
  • Издательство: Синергия

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