Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

作     者：Tianyu Wang Kongyang Chen 

作者机构：Department of Cyberspace SecurityGuangzhou UniversityGuangzhou51006China Institute of Artificial IntelligenceGuangzhou UniversityGuangzhou51006China Center of Young ScholarsPazhou LabGuangzhou510335China 

出 版 物：《Computer Modeling in Engineering & Sciences》 (工程与科学中的计算机建模（英文）)

年 卷 期：2024年第140卷第9期

页      面：2297-2314页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 

基　　金：This work is supported by National Natural Science Foundation of China(Nos.U21A20463,62172117,61802383) Research Project of Pazhou Lab for Excellent Young Scholars(No.PZL2021KF0024) Guangzhou Basic and Applied Basic Research Foundation(Nos.202201010330,202201020162,202201020221) 

主　　题：Crowdsourcing reputation management blockchain 

摘      要：Crowdsourcing holds broad applications in information acquisition and dissemination,yet encounters challenges pertaining to data quality assessment and user reputation *** mechanisms stand as crucial solutions for appraising and updating participant reputation scores,thereby elevating the quality and dependability of crowdsourced ***,these mechanisms face several challenges in traditional crowdsourcing systems:1)platform security lacks robust guarantees and may be susceptible to attacks;2)there exists a potential for large-scale privacy breaches;and 3)incentive mechanisms relying on reputation scores may encounter issues as reputation updates hinge on task demander evaluations,occasionally lacking a dedicated reputation update *** paper introduces a reputation update scheme tailored for crowdsourcing,with a focus on proficiently overseeing participant reputations and alleviating the impact of malicious activities on the sensing ***,the reputation update scheme is determined by an Empirical Cumulative distribution-based Outlier Detection method(ECOD).Our scheme embraces a blockchain-based crowdsourcing framework utilizing a homomorphic encryption method to ensure data transparency and *** of user reputation scores relies on their behavioral history,actively discouraging undesirable ***,we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation,enabling the system to allocate incentives based on user behavior and *** scheme undergoes evaluation on 11 datasets,revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious *** research not only presents a practical solution for crowdsourcing reputation management but also offers valuable insights for future research and applications,holding promise for fostering more reliable and high-quality data collection in