Privacy-Aware and Efficient Mobile Crowdsensing with Truth Discovery
Privacy-Aware and Efficient Mobile Crowdsensing with Truth Discovery
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DOI：
10.1109/tdsc.2017.2753245
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发表时间：
2020-01
影响因子：
7.3
通讯作者：
Yifeng Zheng;Huayi Duan;Xingliang Yuan;Cong Wang-
中科院分区：
文献类型：
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作者：
Yifeng Zheng;Huayi Duan;Xingliang Yuan;Cong Wang-
移动群智感知中的真相发现最近受到广泛关注。它是指从收集的感官数据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的研究，但在隐私感知的移动群智感知中，真相发现仍然很大程度上未被充分探索。由于同态密码系统对大密文的迭代传输和计算，现有的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，我们提出了两种具有真相发现功能的新型隐私感知众感知设计，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相发现过程中的核心原子操作，并相应地精心设计安全设计，以在密文域中实现高效的真相发现。我们的第一个设计是针对单服务器设置进行高度定制的，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，与之前的结果相比，我们的设计在用户通信和计算方面分别节省了至少 30 × 30 美元和 10 10 美元。
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.