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To contain the COVID-19 pandemic, much hope is placed on participatory surveillance using mobile applications such as automated digital contact tracing. However, the privacy risks of such solutions often need to be balanced against their functionalities. This is reflected by an intensive discussion in the public and the scientific community about privacy-preserving approaches. To inform these discussions, we performed a systematic analysis of existing literature on citizen-centered surveillance solutions collecting individual-level spatial data. Our main hypothesis was that there are dependencies between the specific diseases focused on, use cases supported, technology utilized to collect spatial data and data protection measures implemented. We found out that the solutions described were highly specialized and typically focused on a specific combination of a use case, disease and technology. We hence see a large potential for future solutions supporting multiple use cases by combining different technologies (e. g. Bluetooth and GPS). For this to be successful, however, adequate privacy-protection measures must be implemented and we recommend that future solutions should consider the use of modern privacy-enhancing technologies, such as secure multi-party computation protocols or differential privacy.

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