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Closeness: Advanced Privacy for Data by anand_gsoft3603 is a document available to read on EtoBox.

What is Closeness: Advanced Privacy for Data about?

The document presents a new privacy measure called "closeness" to address limitations in k-anonymity and l-diversity when publishing microdata. Closeness requires the distribution of a sensitive attribute in any equivalence class be close to the overall table distribution, using a distance threshold. A more flexible model, (n,t)-closeness, offers higher utility by allowing distance between distributions of up to t. The document discusses desiderata for a distance measure between distributions and presents t

Author

anand_gsoft3603

Language

EN