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Data Anonymization for Data Protection on Publicly Recorded Data

机译:数据匿名化以保护公开记录的数据

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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.
机译:德国的数据保护历史悠久(https://www.goethe.de/en/kul/med/20446236.html)。长期以来,德国联邦数据保护法或德国联邦数据保护法(BDSG)被认为是最严格的方法之一。自2017年5月起,欧盟通用数据保护条例(GDPR)规范了整个欧洲的数据保护,并受到德国法律的强烈影响。在公共区域记录数据时,记录中可能包含个人数据,例如车牌或个人。根据GDPR,此个人数据处理必须满足某些要求才能被视为合法。在本文中,我们致力于在遵守适用法律的情况下在公共场所记录视觉数据。为此,针对移动传感器平台开发了正式的数据保护概念。此数据保护概念的核心部分是个人数据的匿名化,该匿名化是通过基于最新的深度学习的方法实现的,几乎可以实现人类水平的性能。在城市环境中,使用真实的移动传感器平台记录的示例数据对方法进行了定量和定性评估。

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