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SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees

Raffael Bild, Klaus A. Kuhn, Fabian Prasser
2018 Proceedings on Privacy Enhancing Technologies  
In this article, we present a data publishing algorithm that satisfies the differential privacy model.  ...  The transformations performed are truthful, which means that the algorithm does not perturb input data or generate synthetic output data.  ...  ,r m )∈S(D2) m i=1 leaves i (r i ) |Ω i |   = m .SafePub: A Truthful Data Anonymization Algorithm With Strong Privacy Guarantees 85 If g(r) = * appears k times in g(D 1 ), then it is not suppressed in  ... 
doi:10.1515/popets-2018-0004 dblp:journals/popets/BildKP18 fatcat:ahsldstdind6flgx6lum7cvt7q

Production of Categorical Data Verifying Differential Privacy: Conception and Applications to Machine Learning [article]

Héber H. Arcolezi
2022 arXiv   pre-print
To tackle privacy concerns, research communities have proposed different methods to preserve privacy, with Differential privacy (DP) standing out as a formal definition that allows quantifying the privacy-utility  ...  However, because most personal data are sensitive, there is a key challenge in designing privacy-preserving systems.  ...  The differentially private model of ARX was proposed in [120] , namely, SafePub, which produces truthful data output.  ... 
arXiv:2204.00850v1 fatcat:az6x64crzzdnholxqse2lf6xfa