Titlebook: Database Anonymization; Privacy Models, Data Josep Domingo-Ferrer,David Sánchez,Jordi Soria-Com Book 2016 Springer Nature Switzerland AG 20

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https://doi.org/10.1007/978-1-349-19305-9the public sector is pushed to release as much information as possible for the sake of transparency. Organizations releasing data include national statistical institutes (whose core mission is to publish statistical information), healthcare authorities (which occasionally release epidemiologic infor

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Moosa A. Elayah,Laurent A. Lambertn be used by the data controller to empirically evaluate the disclosure risk associated with an anonymized data set. The data protector or controller uses a record linkage algorithm (or several such algorithms) to link each record in the anonymized data with a record in the original data set. Since

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Paula Satne,Krisanna M. Scheiterntifier) attributes, thereby preserving the anonymity of the respondents in the data set. Although the privacy guarantees offered by .-anonymity are limited (i.e., it does not protect against attribute disclosure), its simplicity has made it quite popular. It is sometimes seen as offering a minimal

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Kant on Punishment, Pardon, and Forgivenessd on those principles can be adapted to yield .-closeness by adding the .-closeness constraint in the search for a feasible minimal generalization: in [50] the Incognito algorithm and in [51] the Mondrian algorithm are respectively adapted to .-closeness.

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https://doi.org/10.1057/9780230371576nitially stated as a privacy guarantee in an interactive setting, where queries are submitted to a database containing the original individual records. However, it is general enough to deal with microdata releases. The principle underlying differential privacy is that the presence or absence of any

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