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“Healthy surveillance”: Designing a concept for privacy-preserving mask recognition AI in the age of pandemics [in press]

Kühl, Niklas; Martin, Dominik; Wolff, Clemens; Volkamer, Melanie

Abstract:
The obligation to wear masks in times of pandemics reduces the risk of spreading viruses. In case of the COVID-19 pandemic in 2020, many governments recommended or even obligated their citizens to wear masks as an effective countermeasure. In order to continuously monitor the compliance of this policy measure in public spaces like restaurants or tram stations by public authorities, one scalable and automatable option depicts the application of surveillance systems, i.e., CCTV. However, large-scale monitoring of mask recognition does not only require a well-performing Artificial Intelligence, but also ensure that no privacy issues are introduced, as surveillance is a deterrent for citizens and regulations like General Data Protection Regulation (GDPR) demand strict regulations of such personal data. In this work, we show how a privacy-preserving mask recognition artifact could look like, demonstrate different options for implementation and evaluate performances. Our conceptual deep-learning based Artificial Intelligence is able to achieve detection performances between 95% and 99% in a privacy-friendly setting. On that basis, we elaborate on the trade-off between the level of privacy preservation and Artificial Intelligence performance, i.e. ... mehr

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Postprint §
DOI: 10.5445/IR/1000124961
Frei zugänglich ab 01.01.2023
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Karlsruhe Service Research Institute (KSRI)
Kompetenzzentrum für angewandte Sicherheitstechnologie (KASTEL)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator KITopen-ID: 1000124961
Erschienen in Hawaii International Conference on System Sciences (HICSS 54), 5-8 January 2021, online
Veranstaltung 54 Hawaii International Conference on System Sciences (HICSS 2021), online, 05.01.2021 – 08.01.2021
Projektinformation KASTEL_SKI (BMBF, 16KIS0843)
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