scholarly journals Applying Blockchain Technology to Address the Crisis of Trust During the COVID-19 Pandemic

10.2196/20477 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e20477 ◽  
Author(s):  
Anjum Khurshid
Keyword(s):  
Public Health ◽  
Data Privacy ◽  
Well Being ◽  
Governance Structure ◽  
Privacy Preserving ◽  
Trust Value ◽  
Blockchain Technology ◽  
Trust Networks ◽  
Technology Applications ◽  
Potential Applications

Background The widespread death and disruption caused by the COVID-19 pandemic has revealed deficiencies of existing institutions regarding the protection of human health and well-being. Both a lack of accurate and timely data and pervasive misinformation are causing increasing harm and growing tension between data privacy and public health concerns. Objective This aim of this paper is to describe how blockchain, with its distributed trust networks and cryptography-based security, can provide solutions to data-related trust problems. Methods Blockchain is being applied in innovative ways that are relevant to the current COVID-19 crisis. We describe examples of the challenges faced by existing technologies to track medical supplies and infected patients and how blockchain technology applications may help in these situations. Results This exploration of existing and potential applications of blockchain technology for medical care shows how the distributed governance structure and privacy-preserving features of blockchain can be used to create “trustless” systems that can help resolve the tension between maintaining privacy and addressing public health needs in the fight against COVID-19. Conclusions Blockchain relies on a distributed, robust, secure, privacy-preserving, and immutable record framework that can positively transform the nature of trust, value sharing, and transactions. A nationally coordinated effort to explore blockchain to address the deficiencies of existing systems and a partnership of academia, researchers, business, and industry are suggested to expedite the adoption of blockchain in health care.

scholarly journals Applying Blockchain Technology to Address the Crisis of Trust During the COVID-19 Pandemic (Preprint)

2020 ◽  
Author(s):  
Anjum Khurshid
Keyword(s):  
Public Health ◽  
Data Privacy ◽  
Well Being ◽  
Governance Structure ◽  
Privacy Preserving ◽  
Trust Value ◽  
Blockchain Technology ◽  
Trust Networks ◽  
Technology Applications ◽  
Potential Applications

BACKGROUND The widespread death and disruption caused by the COVID-19 pandemic has revealed deficiencies of existing institutions regarding the protection of human health and well-being. Both a lack of accurate and timely data and pervasive misinformation are causing increasing harm and growing tension between data privacy and public health concerns. OBJECTIVE This aim of this paper is to describe how blockchain, with its distributed trust networks and cryptography-based security, can provide solutions to data-related trust problems. METHODS Blockchain is being applied in innovative ways that are relevant to the current COVID-19 crisis. We describe examples of the challenges faced by existing technologies to track medical supplies and infected patients and how blockchain technology applications may help in these situations. RESULTS This exploration of existing and potential applications of blockchain technology for medical care shows how the distributed governance structure and privacy-preserving features of blockchain can be used to create “trustless” systems that can help resolve the tension between maintaining privacy and addressing public health needs in the fight against COVID-19. CONCLUSIONS Blockchain relies on a distributed, robust, secure, privacy-preserving, and immutable record framework that can positively transform the nature of trust, value sharing, and transactions. A nationally coordinated effort to explore blockchain to address the deficiencies of existing systems and a partnership of academia, researchers, business, and industry are suggested to expedite the adoption of blockchain in health care.

Leveraging blockchain based decentralized apps for the Tokyo Olympics amid the COVID-19 pandemic

First Monday ◽  
2021 ◽  
Author(s):  
Rishi Sabarigirisan ◽  
Aditi Biswas ◽  
Ridhi Rohatgi ◽  
Shyam KC ◽  
Shekhar Shukla
Keyword(s):  
Public Health ◽  
Health And Safety ◽  
Well Being ◽  
Decision Makers ◽  
Contact Tracing ◽  
Strategic Objectives ◽  
Crowd Management ◽  
Blockchain Technology ◽  
Smooth Contact ◽  
Public Health And Safety

The COVID-19 pandemic has induced a cloud of uncertainty over the mega sports event, the 2021 Tokyo Olympics. Cancelling or re-scheduling the event could have serious repercussions on the economic, social and environmental well-being for the involved stakeholders. Thus, it becomes critical to conduct events of this magnitude by adopting appropriate public health measures. In this research, we primarily focus on two main premises relative to public health and safety, contact tracing and crowd management. We explore and evaluate the usability of blockchain based decentralized apps in crowd management and contact tracing for the Tokyo Olympics using value-focused thinking (VFT). A VFT framework aids in narrowing fundamental and strategic objectives that need to be addressed for smooth contact tracing and crowd management by understanding stakeholder viewpoints. We established an equivalence of the objectives identified through VFT with blockchain technology properties. Further, we also present a conceptual ideation of contact tracing and crowd management through blockchain based decentralized apps for the Tokyo Olympics. This work could potentially assist decision-makers, researchers and stakeholders involved in organizing the Tokyo Olympics in understanding and analysing the utility of blockchain based decentralized apps for crowd management and contact tracing.

scholarly journals Trust Hardware Based Secured Privacy Preserving Computation System for Three-Dimensional Data

Electronics ◽  
2021 ◽  
Vol 10 (13) ◽  
pp. 1546
Author(s):  
Munan Yuan ◽  
Xiaofeng Li ◽  
Xiru Li ◽  
Haibo Tan ◽  
Jinlin Xu
Keyword(s):  
Data Privacy ◽  
High Performance ◽  
Distributed Storage ◽  
Three Dimensional ◽  
Privacy Preserving ◽  
Sensitive Data ◽  
Privacy And Security ◽  
Time Consumption ◽  
Blockchain Technology ◽  
3D Data

Three-dimensional (3D) data are easily collected in an unconscious way and are sensitive to lead biological characteristics exposure. Privacy and ownership have become important disputed issues for the 3D data application field. In this paper, we design a privacy-preserving computation system (SPPCS) for sensitive data protection, based on distributed storage, trusted execution environment (TEE) and blockchain technology. The SPPCS separates a storage and analysis calculation from consensus to build a hierarchical computation architecture. Based on a similarity computation of graph structures, the SPPCS finds data requirement matching lists to avoid invalid transactions. With TEE technology, the SPPCS implements a dual hybrid isolation model to restrict access to raw data and obscure the connections among transaction parties. To validate confidential performance, we implement a prototype of SPPCS with Ethereum and Intel Software Guard Extensions (SGX). The evaluation results derived from test datasets show that (1) the enhanced security and increased time consumption (490 ms in this paper) of multiple SGX nodes need to be balanced; (2) for a single SGX node to enhance data security and preserve privacy, an increased time consumption of about 260 ms is acceptable; (3) the transaction relationship cannot be inferred from records on-chain. The proposed SPPCS implements data privacy and security protection with high performance.

scholarly journals Privacy-Preserving Pandemic Monitoring

2021 ◽  
pp. 194-206
Author(s):  
Thu Yein Win ◽  
Hugo Tianfield
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Patient Data ◽  
Future Research ◽  
Health Safety ◽  
Patient Data Privacy ◽  
Significant Challenge ◽  
The World

The recent COVID-19 pandemic has presented a significant challenge for health organisations around the world in providing treatment and ensuring public health safety. While this has highlighted the importance of data sharing amongst them, it has also highlighted the importance of ensuring patient data privacy in doing so. This chapter explores the different techniques which facilitate this, along with their overall implementations. It first provides an overview of pandemic monitoring and the privacy implications associated with it. It then explores the different privacy-preserving approaches that have been used in existing research. It also explores the strengths as well as their limitations, along with possible areas for future research.

scholarly journals Privacy-Preserving K-Nearest Neighbors Training over Blockchain-Based Encrypted Health Data

Electronics ◽  
2020 ◽  
Vol 9 (12) ◽  
pp. 2096
Author(s):  
Rakib Ul Haque ◽  
A S M Touhidul Hasan ◽  
Qingshan Jiang ◽  
Qiang Qu
Keyword(s):  
Data Privacy ◽  
Building Blocks ◽  
Privacy Preserving ◽  
Training Data ◽  
Third Party ◽  
Supervised Machine Learning ◽  
Primary Concern ◽  
Privacy Issue ◽  
Trusted Third Party ◽  
Blockchain Technology

Numerous works focus on the data privacy issue of the Internet of Things (IoT) when training a supervised Machine Learning (ML) classifier. Most of the existing solutions assume that the classifier’s training data can be obtained securely from different IoT data providers. The primary concern is data privacy when training a K-Nearest Neighbour (K-NN) classifier with IoT data from various entities. This paper proposes secure K-NN, which provides a privacy-preserving K-NN training over IoT data. It employs Blockchain technology with a partial homomorphic cryptosystem (PHC) known as Paillier in order to protect all participants (i.e., IoT data analyst C and IoT data provider P) data privacy. When C analyzes the IoT data of P, both participants’ privacy issue arises and requires a trusted third party. To protect each candidate’s privacy and remove the dependency on a third-party, we assemble secure building blocks in secure K-NN based on Blockchain technology. Firstly, a protected data-sharing platform is developed among various P, where encrypted IoT data is registered on a shared ledger. Secondly, the secure polynomial operation (SPO), secure biasing operations (SBO), and secure comparison (SC) are designed using the homomorphic property of Paillier. It shows that secure K-NN does not need any trusted third-party at the time of interaction, and rigorous security analysis demonstrates that secure K-NN protects sensitive data privacy for each P and C. The secure K-NN achieved 97.84%, 82.33%, and 76.33% precisions on BCWD, HDD, and DD datasets. The performance of secure K-NN is precisely similar to the general K-NN and outperforms all the previous state of art methods.

scholarly journals A Review of Artificial Intelligence, Big Data, and Blockchain Technology Applications in Medicine and Global Health

2021 ◽  
Vol 5 (3) ◽  
pp. 41
Author(s):  
Supriya M. ◽  
Vijay Kumar Chattu
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Big Data ◽  
Global Health ◽  
Treatment Protocol ◽  
Diagnostic Procedures ◽  
Well Being ◽  
Medical Practitioners ◽  
Blockchain Technology ◽  
Technology Applications

Artificial intelligence (AI) programs are applied to methods such as diagnostic procedures, treatment protocol development, patient monitoring, drug development, personalized medicine in healthcare, and outbreak predictions in global health, as in the case of the current COVID-19 pandemic. Machine learning (ML) is a field of AI that allows computers to learn and improve without being explicitly programmed. ML algorithms can also analyze large amounts of data called Big data through electronic health records for disease prevention and diagnosis. Wearable medical devices are used to continuously monitor an individual’s health status and store it in cloud computing. In the context of a newly published study, the potential benefits of sophisticated data analytics and machine learning are discussed in this review. We have conducted a literature search in all the popular databases such as Web of Science, Scopus, MEDLINE/PubMed and Google Scholar search engines. This paper describes the utilization of concepts underlying ML, big data, blockchain technology and their importance in medicine, healthcare, public health surveillance, case estimations in COVID-19 pandemic and other epidemics. The review also goes through the possible consequences and difficulties for medical practitioners and health technologists in designing futuristic models to improve the quality and well-being of human lives.

Using Public Health Approaches to Optimize Child Well-Being

2010 ◽  
Author(s):  
Lara R. Robinson ◽  
Camille Smith ◽  
Jennifer W. Kaminski ◽  
Rebecca H. Bitsko ◽  
Angelika H. Claussen ◽  
...  
Keyword(s):  
Public Health ◽  
Well Being

Eating NAFTA

2018 ◽  
Author(s):  
Alyshia Gálvez
Keyword(s):  
Public Health ◽  
Chronic Disease ◽  
Low Income ◽  
Food System ◽  
Dietary Habits ◽  
Trade Agreement ◽  
Well Being ◽  
Health Crisis ◽  
The North ◽  
The World

In the two decades since the North American Free Trade Agreement (NAFTA) went into effect, Mexico has seen an epidemic of diet-related illness. While globalization has been associated with an increase in chronic disease around the world, in Mexico, the speed and scope of the rise has been called a public health emergency. The shift in Mexican foodways is happening at a moment when the country’s ancestral cuisine is now more popular and appreciated around the world than ever. What does it mean for their health and well-being when many Mexicans eat fewer tortillas and more instant noodles, while global elites demand tacos made with handmade corn tortillas? This book examines the transformation of the Mexican food system since NAFTA and how it has made it harder for people to eat as they once did. The book contextualizes NAFTA within Mexico’s approach to economic development since the Revolution, noticing the role envisioned for rural and low-income people in the path to modernization. Examination of anti-poverty and public health policies in Mexico reveal how it has become easier for people to consume processed foods and beverages, even when to do so can be harmful to health. The book critiques Mexico’s strategy for addressing the public health crisis generated by rising rates of chronic disease for blaming the dietary habits of those whose lives have been upended by the economic and political shifts of NAFTA.

Examining the accuracy of estimated smartphone use: How well-being and usage level predict discrepancies between estimated and actual use

2019 ◽  
Author(s):  
Craig Sewall ◽  
Daniel Rosen ◽  
Todd M. Bear
Keyword(s):  
Public Health ◽  
Social Media ◽  
Life Satisfaction ◽  
Mobile Device ◽  
Screen Time ◽  
Well Being ◽  
Actual Use ◽  
Smartphone Use ◽  
Usage Level

The increasing ubiquity of mobile device and social media (SM) use has generated a substantial amount of research examining how these phenomena may impact public health. Prior studies have found that mobile device and SM use are associated with various aspects of well-being. However, a large portion of these studies relied upon self-reported estimates to measure amount of use, which can be inaccurate. Utilizing Apple’s “Screen Time” application to obtain actual iPhone and SM use data, the current study examined the accuracy of self-reported estimates, how inaccuracies bias relationships between use and well-being (depression, loneliness, and life satisfaction), and the degree to which inaccuracies were predicted by levels of well-being. Among a sample of 393 iPhone users, we found that: a.) participants misestimated their weekly overall iPhone and SM use by 22.1 and 16.6 hours, respectively; b.) the correlations between estimated use and well-being variables were consistently stronger than the correlations between actual use and well-being variables; and c.) the amount of inaccuracy in estimated use is associated with levels of participant well-being as well as amount of use. These findings suggest that estimates of device/SM use may be biased by factors that are fundamental to the relationships being investigated. **This manuscript is currently under review**

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