Ackermann, LeonieLeonieAckermann0000-0002-3490-5452Baum, ChristophChristophBaum0000-0001-7012-6010Litvin, AleksandrAleksandrLitvinNicklas, DanielaDanielaNicklasKhalil, Syed IbrahimSyed IbrahimKhalil2024-01-082024-01-082023https://fis.uni-bamberg.de/handle/uniba/92607Estimating visitor frequency in urban areas often relies on camera-based solutions or the active participation of individuals using smartphone applications or devices with RFID or Bluetooth technology. This paper presents the results of a preliminary study on anonymous data collection as a basis for data-driven visitor guidance in Bamberg’s Old Town, using passive, non-intrusive and low-cost Wi-Fi sensors. The study includes a field test installation to evaluate data quality under robust anonymization measures. The data collected as part of the project will be made available to the public in the Mobilithek of the Federal Ministry of Digital Affairs and Transport (BMDV). Still, the collection of Wi-Fi probe requests raises legitimate privacy concerns. We address potential attack models for identifying and tracking devices based on these requests and explain the data collection architecture and Technical Data Protection Concept implemented within CrowdAnym to mitigate these risks. We evaluate the impact of our approach on the quality of the CrowdAnym dataset by comparing the data from one sensor location with the number of people counted by a nearby laser scanner. We are able to show that our approach approximates visitor density well, however the deviation of our data from ground truth increases as visitor frequency increases.engMAC address detectiondatasetsanonymization004conferenceobject10.1145/3615889.3628513