BeepTrace: Blockchain-Enabled Privacy-Preserving Contact Tracing for COVID-19 Pandemic and Beyond

Xu, Hao; Zhang, Lei; Onireti, Oluwakayode; Fang, Yang; Buchanan, William J.; Imran, Muhammad Ali

doi:10.1109/jiot.2020.3025953

Cited by 176 publications

(140 citation statements)

References 12 publications

(14 reference statements)

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“…In such applications, the majority of computation is carried out on user's own devices thereby privacy is preserved [22]. The other notable solutions that have demonstrated effectiveness in terms of privacy are, block chain technology based contact tracing [23,24], combining historical and centralized data [25], and group signature-based apps [26]. All these solu-tions have demonstrated effectiveness to trace the contacts of COVID-19's suspects with privacy guarantees.…”

Section: Latest Technologies/solutions Devised So For To Alleviate Prmentioning

confidence: 99%

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Majeed

Lee

2021

Inventions

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With the advent of the pandemic (e.g., novel corona virus disease 2019 (COVID-19)), a tremendous amount of data about individuals are collected by the health authorities on daily basis for curbing the disease’s spread. The individuals’ data collection/processing at a massive scale for community well-being with the help of digital solutions (e.g., mobile apps for mobility and proximity analysis, contact tracing through credit card usage history, facial recognition through cameras, and crowd analysis using cellular networks data etc.) raise several privacy concerns. Furthermore, the privacy concerns that are arising mainly due to the fine-grained data collection has hindered the response to tackle this pandemic in many countries. Hence, acquiring/handling individuals data with privacy protection has become a vibrant area of research in these pandemic times. This paper explains the shift in privacy paradigm due to the pandemic (e.g., COVID-19) which involves more and detailed data collection about individuals including locations and demographics. We explain technical factors due to which the people’s privacy is at higher risk in the COVID-19 time. In addition, we discuss privacy concerns in different epidemic control measures (ECMs) (e.g., contact tracing, quarantine monitoring, and symptoms reporting etc.) employed by the health authorities to tackle this disease. Further, we provide an insight on the data management in the ECMs with privacy protection. Finally, the future prospects of the research in this area tacking into account the emerging technologies are discussed. Through this brief article, we aim to provide insights about the vulnerability to user’s privacy in pandemic times, likely privacy issues in different ECMs adopted by most countries around the world, how to preserve user’s privacy effectively in all phases of the ECMs considering relevant data in loop, and conceptual foundations of ECMs to fight with future pandemics in a privacy preserving manner.

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Section: Latest Technologies/solutions Devised So For To Alleviate Prmentioning

confidence: 99%

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Majeed

Lee

2021

Inventions

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“…Private Blockchain can provide privacy and accountability for data access as it is able to trace the data operations with trust and decentralization features [26, 117]. Several blockchain-based privacy-preserving solutions have been proposed in the context of COVID-19 pandemic [118, 113]. Federated learning-based ML techniques do not need to share and store data at a centralized location such as a cloud data center.…”

Section: Discussion and Future Challengesmentioning

confidence: 99%

COVID-19 Open Source Data Sets: A Comprehensive Survey

Shuja

Alanazi

Alasmary

et al. 2020

Preprint

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In December 2019, a novel virus named COVID-19 emerged in the city of Wuhan, China. In early 2020, the COVID-19 virus spread in all continents of the world except Antarctica causing widespread infections and deaths due to its contagious characteristics and no medically proven treatment. The COVID-19 pandemic has been termed as the most consequential global crisis after the World Wars. The first line of defense against the COVID-19 spread are the non-pharmaceutical measures like social distancing and personal hygiene. The great pandemic affecting billions of lives economically and socially has motivated the scientific community to come up with solutions based on computer-aided digital technologies for diagnosis, prevention, and estimation of COVID-19. Some of these efforts focus on statistical and Artificial Intelligence-based analysis of the available data concerning COVID- 19. All of these scientific efforts necessitate that the data brought to service for the analysis should be open source to promote the extension, validation, and collaboration of the work in the fight against the global pandemic. Our survey is motivated by the open source efforts that can be mainly categorized as (a) COVID-19 diagnosis from CT scans, X-ray images, and cough sounds, (b) COVID-19 case reporting, transmission estimation, and prognosis from epidemiological, demographic, and mobility data, (c) COVID-19 emotional and sentiment analysis from social media, and (d) knowledge-based discovery and semantic analysis from the collection of scholarly articles covering COVID-19. We survey and compare research works in these directions that are accompanied by open source data and code. Future research directions for data-driven COVID-19 research are also debated. We hope that the article will provide the scientific community with an initiative to start open source extensible and transparent research in the collective fight against the COVID-19 pandemic.

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“…Xu et al. presented BeepTrace [32] , a blockchain-based privacy-preserving contact tracing scheme where pseudonyms are used to achieve the unlinkability between the on-chain data and the real-world identity, and the uploaded on-chain data are also encrypted by the corresponding user. Compared with other existing technicians, BeepTrace achieves privacy protection with a decentralized architecture.…”

Section: Related Work and Motivationsmentioning

confidence: 99%

Privacy-preserving contact tracing in 5G-integrated and blockchain-based medical applications

Zhang

Sharif

et al. 2021

Computer Standards & Interfaces

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The current pandemic situation due to COVID-19 is seriously affecting our daily work and life. To block the propagation of infectious diseases, an effective contact tracing mechanism needs to be implemented. Unfortunately, existing schemes have severe privacy issues that jeopardize the identity-privacy and location-privacy for both users and patients. Although some privacy-preserving systems have been proposed, there remain several issues caused by centralization. To mitigate this issues, we propose a P rivacy-preserving contact T racing scheme in 5G-integrated and B lockchain-based M edical applications, named PTBM. In PTBM, the 5G-integrated network is leveraged as the underlying infrastructure where everyone can perform location checking with his mobile phones or even wearable devices connected to 5G network to find whether they have been in possible contact with a diagnosed patient without violating their privacy. A trusted medical center can effectively trace the patients and their corresponding close contacts. Thorough security and performance analysis show that the proposed PTBM scheme achieves privacy protection, traceability, reliability, and authentication, with high computation & communication efficiency and low latency.

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BeepTrace: Blockchain-Enabled Privacy-Preserving Contact Tracing for COVID-19 Pandemic and Beyond

Cited by 176 publications

References 12 publications

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

Towards Privacy Paradigm Shift Due to the Pandemic: A Brief Perspective

COVID-19 Open Source Data Sets: A Comprehensive Survey

Privacy-preserving contact tracing in 5G-integrated and blockchain-based medical applications

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