Task matching in crowdsourcing has been extensively explored with the increasing popularity of crowdsourcing. However, privacy of tasks and workers is usually ignored in most of exiting solutions. In this paper, we study the problem of privacy-preserving task matching for crowdsourcing with multiple requesters and multiple workers. Instead of utilizing proxy re-encryption, we propose a proxy-free task matching scheme for multi-requester/multi-worker crowdsourcing, which achieves task-worker matching over encrypted data with scalability and non-interaction. We further design two different mechanisms for worker revocation including Server-Local Revocation (SLR) and Global Revocation (GR), which realize efficient worker revocation with minimal overhead on the whole system. The proposed scheme is provably secure in the random oracle model under the Decisional $q$q-Combined Bilinear Diffie-Hellman ($q$q-DCDBH) assumption. Comprehensive theoretical analysis and detailed simulation results show that the proposed scheme outperforms the state-of-the-art work.

中文翻译：

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无代理隐私保护任务匹配与众包中的有效撤销

随着众包的日益普及，众包中的任务匹配得到了广泛的探索。然而，在大多数现有解决方案中，任务和工人的隐私通常被忽略。在本文中，我们研究了具有多个请求者和多个工人的众包隐私保护任务匹配问题。我们没有使用代理重新加密，而是提出了一种用于多请求者/多工作者众包的无代理任务匹配方案，该方案实现了具有可扩展性和非交互性的加密数据的任务-工作者匹配。我们进一步设计了两种不同的工人撤销机制，包括服务器本地撤销（SLR）和全局撤销（GR），它们以最小的整个系统开销实现了有效的工人撤销。$q$q- 组合双线性 Diffie-Hellman ($q$q-DCDBH) 假设。综合理论分析和详细的仿真结果表明，所提出的方案优于最先进的工作。