Identifying Users Across Social Media Networks for Interpretable Fine-Grained Neighborhood Matching by Adaptive GAT

Tang, Wei; Sun, Haifeng; Wang, Jingyu; Li, Cong; Qi, Qi; Wang, Jing; Liao, Jianxin

doi:10.1109/tsc.2023.3288872

Cited by 7 publications

(1 citation statement)

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“…Nevertheless, challenges emerge in the crowdsourcing process concerning the privacy of crowdworkers and the trustworthiness of the information they provide to the crowdsourcers [22]. A novel crowdsourcing network architecture addressing location privacy concerns during task allocation is proposed in [23] by implementing a privacy-preserving, decentralized dispute arbitration protocol to handle payment disputes without revealing users' private information, showcasing resistance to forgery attacks and efficient perfor-mance.…”

Section: Privacy and Trustworthinessmentioning

confidence: 99%

CROWDMATCH: Optimizing Crowdsourcing Matching through the Integration of Matching Theory and Coalition Games

Adesokan,

Kinney,

Tsiropoulou

2024

Future Internet

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This paper tackles the challenges inherent in crowdsourcing dynamics by introducing the CROWDMATCH mechanism. Aimed at enabling crowdworkers to strategically select suitable crowdsourcers while contributing information to crowdsourcing tasks, CROWDMATCH considers incentives, information availability and cost, and the decisions of fellow crowdworkers to model the utility functions for both the crowdworkers and the crowdsourcers. Specifically, the paper presents an initial Approximate CROWDMATCH mechanism grounded in matching theory principles, eliminating externalities from crowdworkers’ decisions and enabling each entity to maximize its utility. Subsequently, the Accurate CROWDMATCH mechanism is introduced, which is initiated by the outcome of the Approximate CROWDMATCH mechanism, and coalition game-theoretic principles are employed to refine the matching process by accounting for externalities. The paper’s contributions include the introduction of the CROWDMATCH system model, the development of both Approximate and Accurate CROWDMATCH mechanisms, and a demonstration of their superior performance through comprehensive simulation results. The mechanisms’ scalability in large-scale crowdsourcing systems and operational advantages are highlighted, distinguishing them from existing methods and highlighting their efficacy in empowering crowdworkers in crowdsourcer selection.

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Section: Privacy and Trustworthinessmentioning

confidence: 99%