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.