{"title":"Blockchain-Driven Privacy-Preserving Contact-Tracing Framework in Pandemics","authors":"Xiao Li;Weili Wu;Tiantian Chen","doi":"10.1109/TCSS.2024.3351191","DOIUrl":null,"url":null,"abstract":"Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage voluntary participation in bearing blockchain costs. Moreover, the absence of framework simulations raises questions about the efficacy of these existing models. To solve above issues, this article introduces a fully third-party independent blockchain-driven contact tracing (BDCT) framework, detailed in its design. The BDCT framework features an Rivest-Shamir-Adleman (RSA) encryption-based transaction verification method (RSA-TVM), achieving over 96% accuracy in contact case recording, even with a 60% probability of individuals failing to verify contact information. Furthermore, we propose a lightweight reputation corrected delegated proof of stake (RC-DPoS) consensus mechanism, coupled with an incentive model, to ensure timely reporting of contact cases while maintaining blockchain decentralization. Additionally, a novel simulation environment for contact tracing is developed, accounting for three distinct contact scenarios with varied population density. Our results and discussions validate the effectiveness, robustness of the RSA-TVM and RC-DPoS, and the low storage demand of the BDCT framework.","PeriodicalId":13044,"journal":{"name":"IEEE Transactions on Computational Social Systems","volume":null,"pages":null},"PeriodicalIF":4.5000,"publicationDate":"2024-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Computational Social Systems","FirstCategoryId":"94","ListUrlMain":"https://ieeexplore.ieee.org/document/10414423/","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, CYBERNETICS","Score":null,"Total":0}
引用次数: 0
Abstract
Blockchain technology, recognized for its decentralized and privacy-preserving capabilities, holds potential for enhancing privacy in contact tracing applications. Existing blockchain-based contact tracing frameworks often overlook one or more critical design details, such as the blockchain data structure, a decentralized and lightweight consensus mechanism with integrated tracing data verification, and an incentive mechanism to encourage voluntary participation in bearing blockchain costs. Moreover, the absence of framework simulations raises questions about the efficacy of these existing models. To solve above issues, this article introduces a fully third-party independent blockchain-driven contact tracing (BDCT) framework, detailed in its design. The BDCT framework features an Rivest-Shamir-Adleman (RSA) encryption-based transaction verification method (RSA-TVM), achieving over 96% accuracy in contact case recording, even with a 60% probability of individuals failing to verify contact information. Furthermore, we propose a lightweight reputation corrected delegated proof of stake (RC-DPoS) consensus mechanism, coupled with an incentive model, to ensure timely reporting of contact cases while maintaining blockchain decentralization. Additionally, a novel simulation environment for contact tracing is developed, accounting for three distinct contact scenarios with varied population density. Our results and discussions validate the effectiveness, robustness of the RSA-TVM and RC-DPoS, and the low storage demand of the BDCT framework.
期刊介绍:
IEEE Transactions on Computational Social Systems focuses on such topics as modeling, simulation, analysis and understanding of social systems from the quantitative and/or computational perspective. "Systems" include man-man, man-machine and machine-machine organizations and adversarial situations as well as social media structures and their dynamics. More specifically, the proposed transactions publishes articles on modeling the dynamics of social systems, methodologies for incorporating and representing socio-cultural and behavioral aspects in computational modeling, analysis of social system behavior and structure, and paradigms for social systems modeling and simulation. The journal also features articles on social network dynamics, social intelligence and cognition, social systems design and architectures, socio-cultural modeling and representation, and computational behavior modeling, and their applications.