A Study on the Security Threats and Privacy Policy of Intelligent Video Surveillance System Considering 5G Network Architecture

Kim, Hyungheon; Cha, Youngkyun; Kim, Taewoo; Kim, Pyeongkang

doi:10.1109/iceic49074.2020.9051302

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…This architecture enables economical use of the resources with the concept of (common) data centres, which are expensive to deploy and maintain. With the fast development of Cloud Computing and 5G networks [175,176] and technologies combined with AI, the function and performance of surveillance systems will be significantly enhanced. Under a deployed 5G network, the data recorded and developed by live sensors could be transformed fast and wirelessly and then stored in a Cloud Centre.…”

Section: About the Next Generation Of Cctv Surveillance Systems For R...mentioning

confidence: 99%

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Zhang

Aftab

Mihaylova

et al. 2022

Sensors

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Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.

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Section: About the Next Generation Of Cctv Surveillance Systems For R...mentioning

confidence: 99%

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Zhang

Aftab

Mihaylova

et al. 2022

Sensors

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“…Examples of security and privacy threats are control takeover attacks that can lead to data manipulation, data loss, data recovery, and data leakage. In addition, malicious third-party MEC applications in the MEC host can cause serious security problems [26,28,29].…”

Section: Related Workmentioning

confidence: 99%

Privacy-Enhanced AKMA for Multi-Access Edge Computing Mobility

Akman

Ginzboorg²,

Damir

et al. 2022

Computers

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Multi-access edge computing (MEC) is an emerging technology of 5G that brings cloud computing benefits closer to the user. The current specifications of MEC describe the connectivity of mobile users and the MEC host, but they have issues with application-level security and privacy. We consider how to provide secure and privacy-preserving communication channels between a mobile user and a MEC application in the non-roaming case. It includes protocols for registration of the user to the main server of the MEC application, renewal of the shared key, and usage of the MEC application in the MEC host when the user is stationary or mobile. For these protocols, we designed a privacy-enhanced version of the 5G authentication and key management for applications (AKMA) service. We formally verified the current specification of AKMA using ProVerif and found a new spoofing attack as well as other security and privacy vulnerabilities. Then we propose a fix against the spoofing attack. The privacy-enhanced AKMA is designed considering these shortcomings. We formally verified the privacy-enhanced AKMA and adapted it to our solution.

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“…Challenges/Issues Discussed [263] Healthcare Security and privacy [264] Healthcare Data intrepretation and fusion [265] Healthcare Security and privacy [266] Healthcare Informed consent [267] Healthcare Informed consent and confidentiality [268] Surveillance Privacy [269] Surveillance Security and privacy [270] Surveillance Privacy [271] Recruitment Privacy and informed consent [272] Generic Security, privacy, bias, and informed consent [273] Generic Informed consent [274] Recruitment Bias [275] Generic Bias [276] Generic Bias [62] Generic Open data, intrepretation, and annotation…”

Section: Ref Applicationmentioning

confidence: 99%

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

Ahmad¹,

Maabreh²,

Ghaly³

et al. 2020

Preprint

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As we make tremendous advances in machine learning and artificial intelligence (ML/AI) technosciences, there is a renewed understanding in the ML/AI community that we must ensure that humans being are at the center of our deliberations so that we don't end in technology-induced dystopias. As strongly argued by Green in his book "Smart Enough City", the incorporation of technology in city environs does not automatically translate into prosperity, wellbeing, urban livability, or social justice. There is a great need to deliberate on the future of the cities worth living and designing. There are philosophical and ethical questions involved along with various challenges that relate to the security, safety, and interpretability of ML/AI algorithms that will form the technological bedrock of future cities. A number of research institutes on humancentered AI (HAI) have been established at top international universities-including Stanford Institute for Human-Centered Artificial Intelligence (HAI), Berkeley's Center for Human-Compatible AI (CHAI), and MIT Institute for Data, Systems, and Society (IDSS). Globally there are calls for technology to be made more humane and human-compatible. For example, Stuart Russell has a book called Human-Compatible AI. The Center for Humane Technology advocates for regulators and technology companies to avoid business models and product features that contribute to social problems such as extremism, polarization, misinformation, and Internet addiction. In this paper, we analyze and explore key challenges including security, robustness, interpretability, and ethical (data and algorithmic) challenges to a successful deployment of AI/ML in human-centric applications, with a particular emphasis on the convergence of these concepts/challenges. We provide a detailed review of existing literature on these key challenges and analyze how one of these challenges may lead to others or help in solving other challenges. The paper also advises on the current limitations, pitfalls, and future directions of research in these domains, and how it can fill the current gaps and lead to better solutions. We believe such rigorous analysis will provide a baseline for future research in the domain.

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A Study on the Security Threats and Privacy Policy of Intelligent Video Surveillance System Considering 5G Network Architecture

Cited by 12 publications

References 4 publications

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention—A Survey

Privacy-Enhanced AKMA for Multi-Access Edge Computing Mobility

Developing Future Human-Centered Smart Cities: Critical Analysis of Smart City Security, Interpretability, and Ethical Challenges

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