Privacy-Preserving Truth Discovery in Crowd Sensing Systems
Privacy-Preserving Truth Discovery in Crowd Sensing Systems
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DOI：
10.1145/3277505
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发表时间：
2019-01
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影响因子：
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通讯作者：
Chenglin Miao;Wenjun Jiang;Lu Su;Yaliang Li;Suxin Guo;Zhan Qin;Houping Xiao;Jing Gao;Kui Ren-K
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文献类型：
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作者：
Chenglin Miao;Wenjun Jiang;Lu Su;Yaliang Li;Suxin Guo;Zhan Qin;Houping Xiao;Jing Gao;Kui Ren-K
最近便携式移动设备的激增催生了人群传感系统。然而，个体参与者提供的感官数据通常并不可靠。为了更好地利用这些感官数据，真相发现的主题引起了人们的广泛关注，其目标是通过质量感知数据聚合来估计用户质量并推断可靠的聚合结果。尽管能够提高聚合准确性，但现有的真相发现方法无法解决个人用户的隐私问题。在本文中，我们提出了一种新颖的隐私保护真相发现（PPTD）框架，它不仅可以保护用户的感官数据，还可以保护通过真相发现方法得出的可靠性分数。该框架的核心思想是使用同态密码系统对用户的加密数据进行加权聚合，既可以保证高精度，又可以保证强隐私保护。为了处理大规模数据，我们还提出使用MapReduce框架并行化PPTD。此外，我们针对以流式方式收集感官数据的场景设计了增量 PPTD 方案。基于两个现实世界的人群感知系统的大量实验表明，所提出的框架可以生成准确的聚合结果，同时保护用户的私人信息。
The recent proliferation of human-carried mobile devices has given rise to the crowd sensing systems. However, the sensory data provided by individual participants are usually not reliable. To better utilize such sensory data, the topic of truth discovery, whose goal is to estimate user quality and infer reliable aggregated results through quality-aware data aggregation, has drawn significant attention. Though able to improve aggregation accuracy, existing truth discovery approaches fail to address the privacy concerns of individual users. In this article, we propose a novel privacy-preserving truth discovery (PPTD) framework, which can protect not only users’ sensory data but also their reliability scores derived by the truth discovery approaches. The key idea of the proposed framework is to perform weighted aggregation on users’ encrypted data using a homomorphic cryptosystem, which can guarantee both high accuracy and strong privacy protection. In order to deal with large-scale data, we also propose to parallelize PPTD with MapReduce framework. Additionally, we design an incremental PPTD scheme for the scenarios where the sensory data are collected in a streaming manner. Extensive experiments based on two real-world crowd sensing systems demonstrate that the proposed framework can generate accurate aggregated results while protecting users’ private information.