Dataset Information

CoNTe: A Core Network Temporal Blockchain for 5G.

ABSTRACT: Virtual Network Functions allow the effective separation between hardware and network functionality, a strong paradigm shift from previously tightly integrated monolithic, vendor, and technology dependent deployments. In this virtualized paradigm, all aspects of network operations can be made to deploy on demand, dynamically scale, as well as be shared and interworked in ways that mirror behaviors of general cloud computing. To date, although seeing rising demand, distributed ledger technology remains largely incompatible in such elastic deployments, by its nature as functioning as an immutable record store. This work focuses on the structural incompatibility of current blockchain designs and proposes a novel, temporal blockchain design built atop federated byzantine agreement, which has the ability to dynamically scale and be packaged as a Virtual Network Function (VNF) for the 5G Core.

SUBMITTER: Platt S

PROVIDER: S-EPMC7570558 | biostudies-literature | 2020 Sep

REPOSITORIES: biostudies-literature

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CoNTe: A Core Network Temporal Blockchain for 5G.

Platt Steven S Sanabria-Russo Luis L Oliver Miquel M

Sensors (Basel, Switzerland) 20200915 18

Virtual Network Functions allow the effective separation between hardware and network functionality, a strong paradigm shift from previously tightly integrated monolithic, vendor, and technology dependent deployments. In this virtualized paradigm, all aspects of network operations can be made to deploy on demand, dynamically scale, as well as be shared and interworked in ways that mirror behaviors of general cloud computing. To date, although seeing rising demand, distributed ledger technology r ...[more]

PMID: 32942759

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Project description:OBJECTIVE:To facilitate clinical/genomic/biomedical research, constructing generalizable predictive models using cross-institutional methods while protecting privacy is imperative. However, state-of-the-art methods assume a "flattened" topology, while real-world research networks may consist of "network-of-networks" which can imply practical issues including training on small data for rare diseases/conditions, prioritizing locally trained models, and maintaining models for each level of the hierarchy. In this study, we focus on developing a hierarchical approach to inherit the benefits of the privacy-preserving methods, retain the advantages of adopting blockchain, and address practical concerns on a research network-of-networks. MATERIALS AND METHODS:We propose a framework to combine level-wise model learning, blockchain-based model dissemination, and a novel hierarchical consensus algorithm for model ensemble. We developed an example implementation HierarchicalChain (hierarchical privacy-preserving modeling on blockchain), evaluated it on 3 healthcare/genomic datasets, as well as compared its predictive correctness, learning iteration, and execution time with a state-of-the-art method designed for flattened network topology. RESULTS:HierarchicalChain improves the predictive correctness for small training datasets and provides comparable correctness results with the competing method with higher learning iteration and similar per-iteration execution time, inherits the benefits of the privacy-preserving learning and advantages of blockchain technology, and immutable records models for each level. DISCUSSION:HierarchicalChain is independent of the core privacy-preserving learning method, as well as of the underlying blockchain platform. Further studies are warranted for various types of network topology, complex data, and privacy concerns. CONCLUSION:We demonstrated the potential of utilizing the information from the hierarchical network-of-networks topology to improve prediction.

Project description:BACKGROUND:Since the beginning of December 2019, the coronavirus disease (COVID-19) has spread rapidly around the world, which has led to increased discussions across online platforms. These conversations have also included various conspiracies shared by social media users. Amongst them, a popular theory has linked 5G to the spread of COVID-19, leading to misinformation and the burning of 5G towers in the United Kingdom. The understanding of the drivers of fake news and quick policies oriented to isolate and rebate misinformation are keys to combating it. OBJECTIVE:The aim of this study is to develop an understanding of the drivers of the 5G COVID-19 conspiracy theory and strategies to deal with such misinformation. METHODS:This paper performs a social network analysis and content analysis of Twitter data from a 7-day period (Friday, March 27, 2020, to Saturday, April 4, 2020) in which the #5GCoronavirus hashtag was trending on Twitter in the United Kingdom. Influential users were analyzed through social network graph clusters. The size of the nodes were ranked by their betweenness centrality score, and the graph's vertices were grouped by cluster using the Clauset-Newman-Moore algorithm. The topics and web sources used were also examined. RESULTS:Social network analysis identified that the two largest network structures consisted of an isolates group and a broadcast group. The analysis also revealed that there was a lack of an authority figure who was actively combating such misinformation. Content analysis revealed that, of 233 sample tweets, 34.8% (n=81) contained views that 5G and COVID-19 were linked, 32.2% (n=75) denounced the conspiracy theory, and 33.0% (n=77) were general tweets not expressing any personal views or opinions. Thus, 65.2% (n=152) of tweets derived from nonconspiracy theory supporters, which suggests that, although the topic attracted high volume, only a handful of users genuinely believed the conspiracy. This paper also shows that fake news websites were the most popular web source shared by users; although, YouTube videos were also shared. The study also identified an account whose sole aim was to spread the conspiracy theory on Twitter. CONCLUSIONS:The combination of quick and targeted interventions oriented to delegitimize the sources of fake information is key to reducing their impact. Those users voicing their views against the conspiracy theory, link baiting, or sharing humorous tweets inadvertently raised the profile of the topic, suggesting that policymakers should insist in the efforts of isolating opinions that are based on fake news. Many social media platforms provide users with the ability to report inappropriate content, which should be used. This study is the first to analyze the 5G conspiracy theory in the context of COVID-19 on Twitter offering practical guidance to health authorities in how, in the context of a pandemic, rumors may be combated in the future.

Dataset Information

CoNTe: A Core Network Temporal Blockchain for 5G.

Publications

CoNTe: A Core Network Temporal Blockchain for 5G.

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