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.

中文翻译：

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在隐私保护众包平台中从任务调整到任务分配

众包平台的专业员工档案可能包含大量可识别和可能敏感的个人信息（例如，个人偏好、技能、可用插槽、可用设备），引起强烈的隐私问题。这导致了隐私保护众包平台的设计，旨在实现高效的众包流程，同时即使在平台不被完全信任的情况下也能提供强大的隐私保证。在本文中，我们提出了两个贡献。首先，我们提出了 PKD 算法，其目标是在保护隐私的众包平台内支持工人资料的大量聚合使用。PKD 算法将同态加密和差分隐私结合在一起，用于计算实际工人技能多维空间的（扰动）分区和每个分区的（扰动）工人 COUNT。其次，我们建议受益于私人信息检索技术的最新进展，以便设计一种既私密又负担得起的任务分配解决方案。我们对使用 PIR 技术向工作人员提出任务的问题进行了深入研究，表明它是 NP-Hard，并提出了 PKD PIR Packing 启发式，根据 PKD 算法的分区输出将任务分组在一起. 简而言之，我们设计了 PKD 算法和 PKD PIR Packing 启发式，我们正式证明了它们对诚实但好奇的工人和/或平台的安全性，