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Ziller, A.* ; Mueller, T.T.* ; Braren, R.* ; Rueckert, D.* ; Kaissis, G.*

Privacy: An axiomatic approach.

Entropy 24:714 (2022)
Verlagsversion DOI PMC
Open Access Gold
Creative Commons Lizenzvertrag
The increasing prevalence of large-scale data collection in modern society represents a potential threat to individual privacy. Addressing this threat, for example through privacy-enhancing technologies (PETs), requires a rigorous definition of what exactly is being protected, that is, of privacy itself. In this work, we formulate an axiomatic definition of privacy based on quantifiable and irreducible information flows. Our definition synthesizes prior work from the domain of social science with a contemporary understanding of PETs such as differential privacy (DP). Our work highlights the fact that the inevitable difficulties of protecting privacy in practice are fundamentally information-theoretic. Moreover, it enables quantitative reasoning about PETs based on what they are protecting, thus fostering objective policy discourse about their societal implementation.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Confidentiality ; Differential Privacy ; Information Flow ; Privacy ; Privacy-enhancing Technologies ; Secrecy
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
e-ISSN 1099-4300
Zeitschrift Entropy
Quellenangaben Band: 24, Heft: 5, Seiten: , Artikelnummer: 714 Supplement: ,
Verlag MDPI
Begutachtungsstatus Peer reviewed
Institut(e) Helmholtz Artifical Intelligence Cooperation Unit (HAICU)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-530014-001
Förderungen German Cancer Research Center
Deutsche Forschungsgemeinschaft
UK Research and Innovation
DOI 10.3390/e24050714
PubMed ID 35626597
Erfassungsdatum 2022-09-13
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