Crowdsourcing is a distributed computing paradigm that utilizes human intelligence or resources from a crowd of workers. Existing solutions of task recommendation in crowdsourcing may leak private and sensitive information about both tasks and workers. To protect privacy, information about tasks and workers should be encrypted before being outsourced to the crowdsourcing platform, which makes the task recommendation a challenging problem. In this paper, we propose a privacy-preserving task recommendation scheme (PPTR) for crowdsourcing, which achieves the task-worker matching while preserving both task privacy and worker privacy. In PPTR, we first exploit the polynomial function to express multiple keywords of task requirements and worker interests. Then, we design a key derivation method based on matrix decomposition, to realize the multi-keyword matching between multiple requesters and multiple workers. Through PPTR, user accountability and user revocation are achieved effectively and efficiently. Extensive privacy analysis and performance evaluation show that PPTR is secure and efficient.

中文翻译：

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用于众包的隐私保护任务推荐服务

众包是一种分布式计算范式，它利用来自一群工人的人类智慧或资源。众包中任务推荐的现有解决方案可能会泄露有关任务和工人的私人和敏感信息。为了保护隐私，关于任务和工人的信息在外包给众包平台之前应该被加密，这使得任务推荐成为一个具有挑战性的问题。在本文中，我们提出了一种用于众包的隐私保护任务推荐方案（PPTR），该方案在保护任务隐私和工人隐私的同时实现了任务-工作者匹配。在PPTR中，我们首先利用多项式函数来表达任务要求和工人兴趣的多个关键字。然后，我们设计了一种基于矩阵分解的密钥推导方法，实现多个请求者和多个工作者之间的多关键字匹配。通过PPTR，有效且高效地实现了用户问责和用户撤销。大量的隐私分析和性能评估表明，PPTR 是安全高效的。