k-Anonymous Crowd Flow Analytics

Measuring pedestrian dynamics using the signals sent from smartphones has become popular. Notably, Wi-Fi-based systems are currently widely deployed. However, many such systems have also become subject to serious debate due to privacy infringement. For some time, secure hashing of a smartphone’s unique MAC address was considered to be sufficient, yet this method has been overruled by Europe’s General Data Protection Regulation which states that an individual should not be identifiable from any dataset without explicit prior consent.

In this paper, we propose a novel anonymization technique that essentially anonymizes detected smartphones immediately at the sensor before any data on such a detection is stored for further analysis. Our solution borrows from the notion of k-anonymity, while avoiding its well-known drawbacks that lead to de-anonymization. Moreover, while ensuring what we coin detection k-anonymity, we also ensure high accuracy of counting measures when dealing with realistic pedestrian flows within crowds. We evaluate our solution both in a simulated environment and in a realistic environment reproducing real-life settings.