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 46 条
  • [31] Data-Matching-Based Privacy-Preserving Statistics and Its Applications in Digital Publishing Industry
    Shen, Hua
    Wu, Ge
    Susilo, Willy
    Zhang, Mingwu
    IEEE TRANSACTIONS ON SERVICES COMPUTING, 2023, 16 (06) : 4554 - 4566
  • [32] Clustering-based Efficient Privacy-preserving Face Recognition Scheme without Compromising Accuracy
    Liu, Meng
    Hu, Hongsheng
    Xiang, Haolong
    Yang, Chi
    Lyu, Lingjuan
    Zhang, Xuyun
    ACM TRANSACTIONS ON SENSOR NETWORKS, 2021, 17 (03)
  • [33] A Distributed Near-Optimal LSH-based Framework for Privacy-Preserving Record Linkage
    Karapiperis, Dimitrios
    Verykios, Vassilios S.
    COMPUTER SCIENCE AND INFORMATION SYSTEMS, 2014, 11 (02) : 745 - 763
  • [34] Privacy-Preserving Friendship Establishment based on Blind Signature and Bloom Filter in Mobile Social Networks
    Zhu, Xiaoyan
    Su, Yang
    Gao, Manfei
    Huang, Yizhe
    2015 IEEE/CIC INTERNATIONAL CONFERENCE ON COMMUNICATIONS IN CHINA (ICCC), 2015,
  • [35] Hashing-Based Distributed Multi-party Blocking for Privacy-Preserving Record Linkage
    Ranbaduge, Thilina
    Vatsalan, Dinusha
    Christen, Peter
    Verykios, Vassilios
    ADVANCES IN KNOWLEDGE DISCOVERY AND DATA MINING, PAKDD 2016, PT II, 2016, 9652 : 415 - 427
  • [36] A Parallel Multi-Party Privacy-Preserving Record Linkage Method Based on a Consortium Blockchain
    Han, Shumin
    Wang, Zikang
    Shen, Dengrong
    Wang, Chuang
    MATHEMATICS, 2024, 12 (12)
  • [37] Score, Arrange, and Cluster: A Novel Clustering-Based Technique for Privacy-Preserving Data Publishing
    Sowmyarani, C. N.
    Namya, L. G.
    Nidhi, G. K.
    Ramakanth Kumar, P.
    IEEE ACCESS, 2024, 12 : 79861 - 79874
  • [38] Enhanced People Counting System based Head-Shoulder Detection in Dense Crowd Scenario
    Abul Hassan, Mohammed
    Pardiansyah, Indratno
    Malik, Aamir Saeed
    Faye, Ibrahima
    Rasheed, Waqas
    2016 6TH INTERNATIONAL CONFERENCE ON INTELLIGENT AND ADVANCED SYSTEMS (ICIAS), 2016,
  • [39] Toward Blockchain-Enabled Privacy-Preserving Data Transmission in Cluster-Based Vehicular Networks
    Joshi, Gyanendra Prasad
    Perumal, Eswaran
    Shankar, K.
    Tariq, Usman
    Ahmad, Tariq
    Ibrahim, Atef
    ELECTRONICS, 2020, 9 (09) : 1 - 15
  • [40] Privacy-preserving HE-based clustering for load profiling over encrypted smart meter data
    Yang, Haomiao
    Liang, Shaopeng
    Zhou, Qixian
    Li, Hongwei
    ICC 2020 - 2020 IEEE INTERNATIONAL CONFERENCE ON COMMUNICATIONS (ICC), 2020,