“Healthy surveillance”: Designing a concept for privacy-preserving mask recognition AI in the age of pandemics. (arXiv:2010.12026v1 [cs.CY])

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”.

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