A firm foundation for statistical disclosure control

The present article reviews the theory of data privacy and confidentiality in statistics and computer science, to modernize the theory of anonymization. This effort results in the mathematical definitions of identity disclosure and attribute disclosure applicable to even synthetic data. Also differe...

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Veröffentlicht in:	Japanese journal of statistics and data science 2020-12, Vol.3 (2), p.721-746
1. Verfasser:	Hoshino, Nobuaki
Format:	Artikel
Sprache:	eng
Schlagworte:	Chemistry and Earth Sciences Computer Science Economics Finance Health Sciences Humanities Insurance Law Management Mathematics and Statistics Medicine Physics Statistical Theory and Methods Statistics Statistics and Computing/Statistics Programs Statistics for Business Statistics for Engineering Statistics for Life Sciences Statistics for Social Sciences Survey Article
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Beschreibung
Zusammenfassung:	The present article reviews the theory of data privacy and confidentiality in statistics and computer science, to modernize the theory of anonymization. This effort results in the mathematical definitions of identity disclosure and attribute disclosure applicable to even synthetic data. Also differential privacy is clarified as a method to bound the accuracy of population inference. This bound is derived by the Hammersley-Chapman-Robbins inequality, and it leads to the intuitive selection of the privacy budget ϵ of differential privacy.
ISSN:	2520-8756 2520-8764
DOI:	10.1007/s42081-020-00086-9