Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/70818

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

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dc.contributor.author Kühl, Niklas
dc.contributor.author Martin, Dominik
dc.contributor.author Wolff, Clemens
dc.contributor.author Volkamer, Melanie
dc.date.accessioned 2020-12-24T19:19:50Z
dc.date.available 2020-12-24T19:19:50Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70818
dc.description.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. the “price of privacy”.
dc.format.extent 10 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Personal Data: Analytics and Management
dc.subject artificial intelligence
dc.subject covid-19
dc.subject mask recognition
dc.subject pandemic
dc.subject privacy-preservation
dc.subject video surveillance
dc.title “Healthy surveillance”: Designing a concept for privacy-preserving mask recognition AI in the age of pandemics
dc.identifier.doi 10.24251/HICSS.2021.206
prism.startingpage 1706
Appears in Collections: Personal Data: Analytics and Management


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