Online Crowd Learning Through Strategic Worker Reports

Article Computer Science, Information Systems

Strategic Information Revelation Mechanism in Crowdsourcing Applications Without Verification

Chao Huang et al.

Summary: We study a crowdsourcing problem where a platform has more information about workers' accuracy and strategically reveals it to incentivize high-quality solutions. We analyze the cases where workers trust or update their beliefs based on the platform's announcement. For naive workers, the platform should announce a high accuracy, while for strategic workers, announcing a lower accuracy may be beneficial. We also show that when the platform is uninformed about workers' prior, increasing the accuracy may paradoxically lead to decreased platform payoff and social welfare.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2023)

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Article Computer Science, Information Systems

Using Truth Detection to Incentivize Workers in Mobile Crowdsourcing

Chao Huang et al.

Summary: This study proposes a novel rewarding mechanism based on truth detection technology to effectively mitigate worker collusion in mobile crowdsourcing platforms. The study also introduces a filtered majority rule for aggregating workers' solutions, outperforming the conventional simple majority rule. Additionally, the impact of truth detection accuracy and workers' imperfect estimation of it on platform decisions are examined.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

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Article Computer Science, Information Systems

Eliciting Information From Heterogeneous Mobile Crowdsourced Workers Without Verification

Chao Huang et al.

Summary: In mobile crowdsourcing, platforms seek to incentivize heterogeneous workers to provide high-quality solutions and truthful reporting. Previous research has not considered the heterogeneity of workers' solution accuracy and the impact on the platform's tradeoff. We address these gaps by studying the interactions between the platform and workers and propose a discriminatory reward policy to improve social welfare.

IEEE TRANSACTIONS ON MOBILE COMPUTING (2022)

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Article Chemistry, Analytical

Mobility-Aware Privacy-Preserving Mobile Crowdsourcing

Guoying Qiu et al.

Summary: The paper introduces a privacy-preserving solution TMarkov for mobile crowdsourcing, which uses time partitioning concept, builds a new transfer model, and conducts unbiased estimation to describe and predict participant's dynamic mobile behaviors.

SENSORS (2021)

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Article Computer Science, Information Systems