Privacy-preserving WiFi-based crowd monitoring

The process of estimating the number of individuals within a defined area, commonly referred to as people counting, is of paramount importance in the realm of safety, security and crisis management. It serves as a crucial tool for accurately monitoring crowd dynamics and facilitating well-informed decision-making during critical situations. In our current study, we place a special emphasis on the utilization of the WiFi fingerprint technique, leveraging probe request messages emitted by smart devices as a proxy for people counting. However, it is essential to recognize the evolving landscape of privacy regulations and the concerted efforts by major smart-device manufacturers to enhance user privacy, exemplified by the introduction of MAC addresses randomization techniques. In this context, we designed a crowd monitoring solution that exploits Bloom filters for ensuring a formal deniability, aligning with the stringent requirements set forth by regulations like the European GDPR. Our proposed solution not only addresses the essential task of people counting but also incorporates advanced privacy-preserving mechanisms. Importantly, it seamlessly integrates with trajectory-based crowd monitoring, offering a comprehensive approach to managing crowds while respecting individual privacy rights. Our study focuses on leveraging WiFi fingerprinting, specifically probe request messages, for accurate people counting and crowd monitoring. Our proposed solution incorporates privacy-preserving measures, such as Bloom filters, to align with evolving privacy regulations, ensuring both effective crowd management and individual privacy rights. image