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Multiobjective Privacy-Preserving Task Assignment in Spatial Crowdsourcing.

Yong-Feng Ge, Hua Wang, Elisa Bertino

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    This summary is machine-generated.

    This study introduces a novel privacy-preserving task assignment framework for spatial crowdsourcing. It enhances worker privacy and optimizes task quality and incentives using a distributed cooperative co-evolutionary multiobjective memetic algorithm.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Data Privacy

    Background:

    • Spatial crowdsourcing relies on location data for task assignment but faces privacy challenges.
    • Existing differential privacy (DP) methods lack personalized privacy and adequate quality control in crowdsourcing.

    Purpose of the Study:

    • To address limitations in current DP spatial crowdsourcing frameworks.
    • To formulate a multiobjective privacy-preserving task assignment (MP-TA) problem.
    • To maximize worker incentives and task quality while ensuring personalized privacy.

    Main Methods:

    • A three-phase framework: worker proposal, candidate selection, and task assignment optimization.
    • Development of a distributed cooperative co-evolutionary multiobjective memetic algorithm (DCC-MMA).
    • Implementation of matching-based crossover, mutation, and fix operations for enhanced search.

    Main Results:

    • The proposed DCC-MMA algorithm generates high-quality solutions for both incentive and quality objectives.
    • Demonstrated superiority in solution quality, convergence speed, and scalability over existing algorithms.
    • Successfully integrates personalized privacy preservation with task assignment optimization.

    Conclusions:

    • The MP-TA problem formulation and DCC-MMA offer a robust solution for privacy-preserving spatial crowdsourcing.
    • The framework effectively balances worker privacy, incentives, and task quality.
    • DCC-MMA shows significant improvements in efficiency and scalability for complex optimization tasks.