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Privacy-preserving WiFi-based Crowd Monitoring
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  • Riccardo Rusca,
  • Alex Carluccio,
  • Claudio Casetti,
  • Paolo Giaccone
Riccardo Rusca
Politecnico di Torino Dipartimento di Automatica e Informatica

Corresponding Author:[email protected]

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Alex Carluccio
Politecnico di Torino Dipartimento di Automatica e Informatica
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Claudio Casetti
Politecnico di Torino Dipartimento di Automatica e Informatica
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Paolo Giaccone
Consorzio Nazionale Interuniversitario per le Telecomunicazioni
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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 [1] . 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.
10 Nov 2023Submitted to Transactions on Emerging Telecommunications Technologies
10 Nov 2023Assigned to Editor
10 Nov 2023Submission Checks Completed
10 Nov 2023Review(s) Completed, Editorial Evaluation Pending
14 Nov 2023Reviewer(s) Assigned