Preliminary Results from a Real-World Trial of a Privacy-Preserving Crowd-Flow Sensor Network in Freiburg, Germany

Abstract

Estimating the crowd size and flow within inner cities and events provides crucial information for venue organizers, emergency services, and city planners. However, deploying such solutions can be a sensitive issue, since many solutions are in conflict with the GDPR. In this work, we present an architectural concept of a privacy-preserving crowd flow network based on cooperative Bluetooth Low Energy advertisements. At the core of the network is a privacy-enhancing technology, the Bloom filter, which ensures anonymization of collected data while still enabling crowd flow measurement. The sensor network has been deployed in a real-world setting, and preliminary data collected over the span of four weeks at the Christmas market in Freiburg, Germany, are presented. Besides the estimation of nearby devices, the preliminary measurement data demonstrate the feasibility of crowd flow analytics.