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
  • [41] Enabling Privacy-Preserving Incentives for Mobile Crowd Sensing Systems
    Jin, Haiming
    Su, Lu
    Ding, Bolin
    Nahrstedt, Klara
    Borisov, Nikita
    PROCEEDINGS 2016 IEEE 36TH INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING SYSTEMS ICDCS 2016, 2016, : 344 - 353
  • [42] Privacy-preserving Crowd-sensing for Dynamic Spectrum Access
    Troja, Erald
    Gitter, Joshua
    MILCOM 2019 - 2019 IEEE MILITARY COMMUNICATIONS CONFERENCE (MILCOM), 2019,
  • [43] Scalable Privacy-Preserving Participant Selection in Mobile Crowd Sensing
    Li, Ting
    Jung, Taeho
    Li, Hanshang
    Cao, Lijuan
    Wang, Weichao
    Li, Xiang-Yang
    Wang, Yu
    2017 IEEE INTERNATIONAL CONFERENCE ON PERVASIVE COMPUTING AND COMMUNICATIONS (PERCOM), 2017,
  • [44] Location privacy-preserving Mobile Crowd Sensing with Anonymous Reputation
    Mille, Arthur
    Karim, Lutful
    Almhana, Jalal
    Khan, Nargis
    2020 16TH INTERNATIONAL WIRELESS COMMUNICATIONS & MOBILE COMPUTING CONFERENCE, IWCMC, 2020, : 1812 - 1817
  • [45] Privacy-Preserving Mobile Crowd Sensing for Big Data Applications
    Shen, Wenlong
    Yin, Bo
    Cheng, Yu
    Cao, Xianghui
    Li, Qing
    2017 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2017,
  • [46] Research on Incentive Mechanism with Privacy-Preserving in Mobile Crowd Sensing
    Liang Y.
    An J.
    Hu X.-Z.
    Yang Q.
    Si H.-F.
    Jisuanji Xuebao/Chinese Journal of Computers, 2020, 43 (12): : 2414 - 2431
  • [47] Location Privacy-Preserving Truth Discovery in Mobile Crowd Sensing
    Gao, Jingsheng
    Fu, Shaojing
    Luo, Yuchuan
    Xie, Tao
    2020 29TH INTERNATIONAL CONFERENCE ON COMPUTER COMMUNICATIONS AND NETWORKS (ICCCN 2020), 2020,
  • [48] Location Privacy-Preserving Mobile Crowd Sensing with Anonymous Reputation
    Yi, Xun
    Lam, Kwok-Yan
    Bertino, Elisa
    Rao, Fang-Yu
    COMPUTER SECURITY - ESORICS 2019, PT II, 2019, 11736 : 387 - 411
  • [49] Hiding outliers into crowd: Privacy-preserving data publishing with outliers
    Wang, Hui
    Liu, Ruilin
    DATA & KNOWLEDGE ENGINEERING, 2015, 100 : 94 - 115
  • [50] Privacy-preserving mobile crowd sensing in ad hoc networks
    Wang, Zhijie
    Huang, Dijiang
    AD HOC NETWORKS, 2018, 73 : 14 - 26