Riccardo Rusca, Diego Gasco, Claudio Casetti, Paolo Giaccone
{"title":"用于人群管理的基于 WiFi 指纹的隐私保护人员计数系统","authors":"Riccardo Rusca, Diego Gasco, Claudio Casetti, Paolo Giaccone","doi":"10.1016/j.comcom.2024.07.010","DOIUrl":null,"url":null,"abstract":"<div><p>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 <strong>ARGO</strong>, 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.</p></div>","PeriodicalId":55224,"journal":{"name":"Computer Communications","volume":"225 ","pages":"Pages 339-349"},"PeriodicalIF":4.5000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S0140366424002482/pdfft?md5=9457ab8d762cad7b7fa0ca64c873b035&pid=1-s2.0-S0140366424002482-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Privacy-preserving WiFi fingerprint-based people counting for crowd management\",\"authors\":\"Riccardo Rusca, Diego Gasco, Claudio Casetti, Paolo Giaccone\",\"doi\":\"10.1016/j.comcom.2024.07.010\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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 <strong>ARGO</strong>, 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.</p></div>\",\"PeriodicalId\":55224,\"journal\":{\"name\":\"Computer Communications\",\"volume\":\"225 \",\"pages\":\"Pages 339-349\"},\"PeriodicalIF\":4.5000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S0140366424002482/pdfft?md5=9457ab8d762cad7b7fa0ca64c873b035&pid=1-s2.0-S0140366424002482-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer Communications\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0140366424002482\",\"RegionNum\":3,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INFORMATION SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer Communications","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0140366424002482","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
Privacy-preserving WiFi fingerprint-based people counting for crowd management
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.
期刊介绍:
Computer and Communications networks are key infrastructures of the information society with high socio-economic value as they contribute to the correct operations of many critical services (from healthcare to finance and transportation). Internet is the core of today''s computer-communication infrastructures. This has transformed the Internet, from a robust network for data transfer between computers, to a global, content-rich, communication and information system where contents are increasingly generated by the users, and distributed according to human social relations. Next-generation network technologies, architectures and protocols are therefore required to overcome the limitations of the legacy Internet and add new capabilities and services. The future Internet should be ubiquitous, secure, resilient, and closer to human communication paradigms.
Computer Communications is a peer-reviewed international journal that publishes high-quality scientific articles (both theory and practice) and survey papers covering all aspects of future computer communication networks (on all layers, except the physical layer), with a special attention to the evolution of the Internet architecture, protocols, services, and applications.