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From Task Tuning to Task Assignment in Privacy-Preserving Crowdsourcing Platforms

Abstract : Specialized worker profiles of crowdsourcing platforms may contain a large amount of identifying and possibly sensitive personal information (e.g., personal preferences, skills, available slots, available devices) raising strong privacy concerns. This led to the design of privacy-preserving crowdsourcing platforms, that aim at enabling efficient crowd-sourcing processes while providing strong privacy guarantees even when the platform is not fully trusted. In this paper, we propose two contributions. First, we propose the PKD algorithm with the goal of supporting a large variety of aggregate usages of worker profiles within a privacy-preserving crowdsourcing platform. The PKD algorithm combines together homomorphic encryption and differential privacy for computing (perturbed) partitions of the multi-dimensional space of skills of the actual population of workers and a (perturbed) COUNT of workers per partition. Second, we propose to benefit from recent progresses in Private Information Retrieval techniques in order to design a solution to task assignment that is both private and affordable. We perform an in-depth study of the problem of using PIR techniques for proposing tasks to workers, show that it is NP-Hard, and come up with the PKD PIR Packing heuristic that groups tasks together according to the partitioning output by the PKD algorithm. In a nutshell, we design the PKD algorithm and the PKD PIR Packing heuristic, we prove formally their security against honest-but-curious workers and/or platform, we analyze their complexities, and we demonstrate their quality and affordability in real-life scenarios through an extensive experimental evaluation performed over both synthetic and realistic datasets.
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Submitted on : Thursday, October 1, 2020 - 10:39:13 AM
Last modification on : Friday, August 5, 2022 - 2:54:52 PM


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Joris Duguépéroux, Tristan Allard. From Task Tuning to Task Assignment in Privacy-Preserving Crowdsourcing Platforms. Transactions on Large-Scale Data- and Knowledge-Centered Systems, 2020, pp.67-107. ⟨10.1007/978-3-662-62271-1_3⟩. ⟨hal-02896342v2⟩



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