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Computer Science > Cryptography and Security

arXiv:2108.08275 (cs)
COVID-19 e-print

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[Submitted on 9 Aug 2021]

Title:TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing

Authors:Mohammad Salimibeni, Zohreh Hajiakhondi-Meybodi, Arash Mohammadi, Yingxu Wang
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Abstract:Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones. Since the spreading probability of the novel coronavirus in indoor environments is much higher than that of the outdoors, there is an urgent and unmet quest to develop/design efficient, autonomous, trustworthy, and secure indoor CT solutions. Despite such an urgency, this field is still in its infancy. The paper addresses this gap and proposes the Trustworthy Blockchain-enabled system for Indoor Contact Tracing (TB-ICT) framework. The TB-ICT framework is proposed to protect privacy and integrity of the underlying CT data from unauthorized access. More specifically, it is a fully distributed and innovative blockchain platform exploiting the proposed dynamic Proof of Work (dPoW) credit-based consensus algorithm coupled with Randomized Hash Window (W-Hash) and dynamic Proof of Credit (dPoC) mechanisms to differentiate between honest and dishonest nodes. The TB-ICT not only provides a decentralization in data replication but also quantifies the node's behavior based on its underlying credit-based mechanism. For achieving high localization performance, we capitalize on availability of Internet of Things (IoT) indoor localization infrastructures, and develop a data driven localization model based on Bluetooth Low Energy (BLE) sensor measurements. The simulation results show that the proposed TB-ICT prevents the COVID-19 from spreading by implementation of a highly accurate contact tracing model while improving the users' privacy and security.
Subjects: Cryptography and Security (cs.CR); Machine Learning (cs.LG); Signal Processing (eess.SP)
Cite as: arXiv:2108.08275 [cs.CR]
  (or arXiv:2108.08275v1 [cs.CR] for this version)
  https://doi.org/10.48550/arXiv.2108.08275

Submission history

From: Arash Mohammadi [view email]
[v1] Mon, 9 Aug 2021 17:27:49 UTC (3,930 KB)
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