IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-Enabled Architecture for Contact Tracing

Tedeschi, Pietro; Bakiras, Spiridon; Pietro, Roberto Di

doi:10.1109/mcom.001.2000729

Cited by 26 publications

(13 citation statements)

References 6 publications

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“… 4 [22] Efficient and Secure Contact Tracing Discussion based on the solution of privacy-preserving contact tracing and challenges faced by it. 5 [23] Use of UAVs and Drone for the protection in this pandemic Potential technologies like AI, ML, and IoT in the field of health care are reviewed for the prediction of the pandemic, disease analysis, delivery of medicine, etc. 6 [24] Contact tracing via TraceTogether App Demonstration of a mobile application enabled by IoT for contact tracing of a coronavirus disease.…”

Section: Iot Technologies In Healthcare During the Covid-19 Pandemicmentioning

confidence: 99%

Applications of cognitive internet of medical things in modern healthcare

Jabbar¹,

Shandilya²,

Kumar³

et al. 2022

Computers and Electrical Engineering

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Section: Iot Technologies In Healthcare During the Covid-19 Pandemicmentioning

confidence: 99%

Applications of cognitive internet of medical things in modern healthcare

Jabbar¹,

Shandilya²,

Kumar³

et al. 2022

Computers and Electrical Engineering

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“…This exposes the learning model to a large number of heterogeneous data samples, each representing a unique behaviour of benign and malicious logs. However, this method does not consider security aspects or respect users' privacy, due to the requirement of collecting and sharing logs centrally [28]. This often exposes sensitive information and behaviour in the case of IoT data.…”

Section: Federated Learningmentioning

confidence: 99%

HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection

Sarhan¹,

Lo²,

Layeghy³

et al. 2022

Preprint

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The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. The utilisation of Machine Learning (ML) capabilities in the defence against IoT cyber attacks has many potential benefits. However, the currently proposed frameworks do not consider data privacy, secure architectures, and/or scalable deployments of IoT ecosystems. In this paper, we propose a hierarchical blockchain-based federated learning framework to enable secure and privacy-preserved collaborative IoT intrusion detection. We highlight and demonstrate the importance of sharing cyber threat intelligence among inter-organisational IoT networks to improve the model's detection capabilities. The proposed ML-based intrusion detection framework follows a hierarchical federated learning architecture to ensure the privacy of the learning process and organisational data. The transactions (model updates) and processes will run on a secure immutable ledger, and the conformance of executed tasks will be verified by the smart contract. We have tested our solution and demonstrated its feasibility by implementing it and evaluating the intrusion detection performance using a key IoT data set. The outcome is a securely designed ML-based intrusion detection system capable of detecting a wide range of malicious activities while preserving data privacy.

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“…Recently, this was observed in the applications implementing the decentralized privacy-preserving proximity-tracing (DP-3T) protocol [32]. While the protocol only results in the broadcast of anonymized beacons, without providing the direct possibility of correlating beacons to an individual, because of other applications that may not be so careful [33], or the work of security researchers that explore specific situations that are not trivial to reproduce, citizens may be highly suspicious of using tracking devices. Interestingly, telecom operators and several third parties have managed to keep their image clean, while gathering massive amounts of personal data [34].…”

Section: Background and Related Workmentioning

confidence: 99%

Security Mechanisms of a Mobile Health Application for Promoting Physical Activity among Older Adults

Bastos

Ribeiro

Silva

et al. 2021

Sensors

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Physical activity contributes to the maintenance of health conditions and functioning. However, the percentage of older adults who comply with the recommendations for physical activity levels is low when compared to the same percentages on younger groups. The SmartWalk system aims to encourage older adults to perform physical activity (i.e., walking in the city), which is monitored and adjusted by healthcare providers for best results. The study reported in this article focused on the implementation of SmartWalk security services to keep personal data safe during communications and while at rest, which were validated considering a comprehensive use case. The security framework offers various mechanisms, including an authentication system that was designed to complement the pairs of usernames and passwords with trusted execution environments and token-based features, authorization with different access levels, symmetric and asymmetric key cryptography, critical transactions review, and logging supported by blockchain technology. The resulting implementation contributes for a common understanding of the security features of trustful smart cities’ applications, which conforms with existing legislation and regulations.

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IoTrace: A Flexible, Efficient, and Privacy-Preserving IoT-Enabled Architecture for Contact Tracing

Cited by 26 publications

References 6 publications

Applications of cognitive internet of medical things in modern healthcare

Applications of cognitive internet of medical things in modern healthcare

HBFL: A Hierarchical Blockchain-based Federated Learning Framework for a Collaborative IoT Intrusion Detection

Security Mechanisms of a Mobile Health Application for Promoting Physical Activity among Older Adults

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