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

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Title:“Healthy surveillance”: Designing a concept for privacy-preserving mask recognition AI in the age of pandemics
Authors:Kühl, Niklas
Martin, Dominik
Wolff, Clemens
Volkamer, Melanie
Keywords:Personal Data: Analytics and Management
artificial intelligence
mask recognition
show 2 moreprivacy-preservation
video surveillance
show less
Date Issued:05 Jan 2021
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”.
Pages/Duration:10 pages
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections: Personal Data: Analytics and Management

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