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Data Anonymization for Data Protection on Publicly Recorded Data

 
: Münch, D.; Grosselfinger, A.-K.; Krempel, E.; Hebel, M.; Arens, M.

:

Tzovaras, Dimitrios:
Computer vision systems. 12th International Conference, ICVS 2019. Proceedings : Thessaloniki, Greece, September 23-25, 2019
Cham: Springer Nature, 2019 (Lecture Notes in Computer Science 11754)
ISBN: 978-3-030-34994-3 (Print)
ISBN: 978-3-030-34995-0 (Online)
S.245-258
International Conference on Computer Vision Systems (ICVS) <12, 2019, Thessaloniki>
Englisch
Konferenzbeitrag
Fraunhofer IOSB ()

Abstract
Data protection in Germany has a long tradition (https://www.goethe.de/en/kul/med/20446236.html). For a long time, the German Federal Data Protection Act or Bundesdatenschutzgesetz (BDSG) was considered as one of the strictest. Since May 2017 the EU General Data Protection Regulation (GDPR) regulates data protection all over Europe and it strongly influenced by the German law. When recording data in public areas, the recordings may contain personal data, such as license plates or persons. According to the GDPR this processing of personal data has to fulfill certain requirements to be considered lawful. In this paper, we address recording visual data in public while abiding by the applicable laws. Towards this end, a formal data protection concept is developed for a mobile sensor platform. The core part of this data protection concept is the anonymization of personal data, which is implemented with state-of-the-art deep learning based methods achieving almost human-level performance. The methods are evaluated quantitatively and qualitatively on example data recorded with a real mobile sensor platform in an urban environment.

: http://publica.fraunhofer.de/dokumente/N-628999.html