Truth discovery in mobile crowdsensing has recently received wide attention. It refers to the procedure for estimating the unknown user reliability from collected sensory data and inferring truthful information via reliability-aware data aggregation. Though widely studied in the plaintext domain, truth discovery remains largely under-explored in privacy-aware mobile crowdsensing. Existing works either do not consider user reliability issue or fall short of achieving practical cost efficiency, due to iterative transmission and computation over large ciphertexts from homomorphic cryptosystem. In this paper, we propose two new privacy-aware crowdsensing designs with truth discovery that significantly improve the bandwidth and computation performance on individual users. Our insight is to identify the core atomic operation in the iterative truth discovery procedure, and carefully craft security designs accordingly to enable efficient truth discovery in the ciphertext domain. Our first design is highly customized for the single-server setting, while our second design under the two-server model further shifts most of user workloads to the cloud server side. Both our designs protect individual sensory data and reliability degrees throughout the truth discovery procedure. Experiments show that compared with the prior result, our designs gain at least $30 \times$30× and $10 \times$10× savings on user communication and computation, respectively.

中文翻译：

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具有隐私意识且高效的移动人群感知与真相发现

移动人群感知中的真相发现最近受到了广泛关注。它是指从收集的感官数据中估计未知用户可靠性并通过可靠性感知数据聚合推断真实信息的过程。尽管在明文领域得到了广泛研究，但在具有隐私意识的移动人群感知中，真相发现在很大程度上仍未得到充分探索。由于对来自同态密码系统的大型密文进行迭代传输和计算，现有工作要么没有考虑用户可靠性问题，要么无法实现实际的成本效率。在本文中，我们提出了两种新的具有真实发现功能的隐私感知人群感知设计，可显着提高个人用户的带宽和计算性能。我们的见解是识别迭代真理发现过程中的核心原子操作，并相应地精心设计安全设计，以实现密文域中的有效真理发现。我们的第一个设计针对单服务器设置进行了高度定制，而我们在双服务器模型下的第二个设计进一步将大部分用户工作负载转移到了云服务器端。我们的两种设计都可以在整个真相发现过程中保护个人感官数据和可靠性。实验表明，与先前的结果相比，我们的设计至少获得了 而我们在双服务器模型下的第二个设计将大部分用户工作负载进一步转移到云服务器端。我们的两种设计都可以在整个真相发现过程中保护个人感官数据和可靠性。实验表明，与先前的结果相比，我们的设计至少获得了 而我们在双服务器模型下的第二个设计将大部分用户工作负载进一步转移到云服务器端。我们的两种设计都可以在整个真相发现过程中保护个人感官数据和可靠性。实验表明，与先前的结果相比，我们的设计至少获得了$30 \times$30× 和 $10 \times$10× 分别节省用户通信和计算。