Privacy-preserving WiFi fingerprint-based people counting for crowd management

被引:0
|
作者
Rusca, Riccardo [1 ]
Gasco, Diego [1 ]
Casetti, Claudio [1 ]
Giaccone, Paolo [1 ]
机构
[1] Politecn Torino, Corso Duca Abruzzi 24, I-10129 Turin, Italy
关键词
Crowd monitoring; People counting; WiFi; Probe request; Bloom filter; Anonymization noise; DBSCAN; Clustering;
D O I
10.1016/j.comcom.2024.07.010
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The practice of people counting serves as an indispensable tool for meticulously monitoring crowd dynamics, enabling informed decision-making in critical situations, and optimizing the management of urban spaces, facilities, and services. Beyond its fundamental role in safety and security, tracking people's flows has evolved into a necessity for diverse business applications and the effective administration of both outdoor and indoor urban environments. In the ongoing exploration of the study, emphasis is placed on employing a passive counting technique. This method leverages WiFi probe request messages emitted by smart devices to assess the number of devices, providing a reliable estimate of the number of people in a specific area. However, it is crucial to acknowledge the dynamic landscape of privacy regulations and the concerted efforts by leading smart-device manufacturers to fortify user privacy, as evidenced by the adoption of MAC address randomization. In response to these considerations, an enhanced iteration of the WiFi traffic generator has been introduced. This upgraded version is designed to generate realistic datasets with ground truth, aligning with the evolving privacy landscape. Additionally, leveraging a profound understanding of probe requests and the capabilities of the designed generator, a novel crowd monitoring solution that incorporates machine learning techniques, named ARGO, has been developed. This innovative approach effectively addresses challenges posed by randomized MAC addresses, incorporating Bloom filters to ensure a formal "deniability"that complies with stringent regulations, including the European GDPR (European Parliament, Council of the European Union, Regulation (EU), 2016). The proposed solution adeptly addresses the pivotal task of people counting by harnessing WiFi probe request messages. Significantly, it prioritizes users' privacy, aligning with the foundational principles outlined in regulations such as the European GDPR.
引用
收藏
页码:339 / 349
页数:11
相关论文
共 50 条
  • [1] Privacy-preserving crowdsourced site survey in WiFi fingerprint-based localization
    Li, Shujun
    Li, Hong
    Sun, Limin
    EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2016,
  • [2] Privacy-preserving crowdsourced site survey in WiFi fingerprint-based localization
    Shujun Li
    Hong Li
    Limin Sun
    EURASIP Journal on Wireless Communications and Networking, 2016
  • [3] A Lightweight Location Privacy-Preserving Scheme for WiFi Fingerprint-Based Localization
    Li, Hong
    He, Yunhua
    Cheng, Xiuzhen
    Sun, Limin
    2016 INTERNATIONAL CONFERENCE ON IDENTIFICATION, INFORMATION AND KNOWLEDGE IN THE INTERNET OF THINGS (IIKI), 2016, : 525 - 529
  • [4] Privacy-preserving WiFi-based crowd monitoring
    Rusca, Riccardo
    Carluccio, Alex
    Casetti, Claudio
    Giaccone, Paolo
    TRANSACTIONS ON EMERGING TELECOMMUNICATIONS TECHNOLOGIES, 2024, 35 (03)
  • [5] FAPRIL: Towards Faster Privacy-preserving Fingerprint-based Localization
    van der Beets, Christopher
    Nieminen, Raine
    Schneider, Thomas
    SECRYPT : PROCEEDINGS OF THE 19TH INTERNATIONAL CONFERENCE ON SECURITY AND CRYPTOGRAPHY, 2022, : 108 - 120
  • [6] Efficient Privacy-Preserving Fingerprint-based Indoor Localization using Crowdsourcing
    Armengol, Patrick
    Tobkes, Rachelle
    Akkaya, Kemal
    Ciftler, Bekir S.
    Guvenc, Ismail
    2015 IEEE 12TH INTERNATIONAL CONFERENCE ON MOBILE AD HOC AND SENSOR SYSTEMS (MASS), 2015, : 549 - 554
  • [7] Privacy-preserving WiFi Fingerprint Localization Based on Spatial Linear Correlation
    Yang, Xu
    Luo, Yuchuan
    Xu, Ming
    Fu, Shaojing
    Chen, Yingwen
    WIRELESS ALGORITHMS, SYSTEMS, AND APPLICATIONS (WASA 2022), PT I, 2022, 13471 : 401 - 412
  • [8] Federated Learning for RSS Fingerprint-based Localization: A Privacy-Preserving Crowdsourcing Method
    Ciftler, Bekir Sait
    Albaseer, Abdullatif
    Lasla, Noureddine
    Abdallah, Mohamed
    2020 16TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE, IWCMC, 2020, : 2112 - 2117
  • [9] Achieving Privacy Preservation in WiFi Fingerprint-Based Localization
    Li, Hong
    Sun, Limin
    Zhu, Haojin
    Lu, Xiang
    Cheng, Xiuzhen
    2014 PROCEEDINGS IEEE INFOCOM, 2014, : 2337 - 2345
  • [10] Efficient Privacy-Preserving Fingerprint-Based Authentication System Using Fully Homomorphic Encryption
    Kim, Taeyun
    Oh, Yongwoo
    Kim, Hyoungshick
    SECURITY AND COMMUNICATION NETWORKS, 2020, 2020