Open Access

ARTICLE

Blockchain-Assisted Unsupervised Learning Method for Crowdsourcing Reputation Management

by Tianyu Wang^1,2, Kongyang Chen^2,3,*

1 Department of Cyberspace Security, Guangzhou University, Guangzhou, 51006, China
2 Institute of Artificial Intelligence, Guangzhou University, Guangzhou, 51006, China
3 Center of Young Scholars, Pazhou Lab, Guangzhou, 510335, China

* Corresponding Author: Kongyang Chen. Email:

Computer Modeling in Engineering & Sciences 2024, 140(3), 2297-2314. https://doi.org/10.32604/cmes.2024.049964

Received 23 January 2024; Accepted 22 March 2024; Issue published 08 July 2024

Abstract

Crowdsourcing holds broad applications in information acquisition and dissemination, yet encounters challenges pertaining to data quality assessment and user reputation management. Reputation mechanisms stand as crucial solutions for appraising and updating participant reputation scores, thereby elevating the quality and dependability of crowdsourced data. However, 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 module. This 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 system. Here, 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 tamper-resistance. Computation of user reputation scores relies on their behavioral history, actively discouraging undesirable conduct. Additionally, we introduce a dynamic weight incentive mechanism that mirrors alterations in participant reputation, enabling the system to allocate incentives based on user behavior and reputation. Our scheme undergoes evaluation on 11 datasets, revealing substantial enhancements in data credibility for crowdsourcing systems and a reduction in the influence of malicious behavior. This 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 crowdsourcing across diverse domains.

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Keywords

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