Abstract Truth discovery is a reliable and effective technique to resolve conflicts of heterogeneous data and estimate user reliability in mobile crowdsensing systems. Despite its effectiveness, the widespread adoption of truth discovery requires solid privacy preservation against users’ sensory data and reliability information. Existing works of private truth discovery are primarily based on conventional cryptographic primitives, which introduce tremendous workloads on the system. In this work, we first propose an efficient and privacy-preserving truth discovery framework (EPTD-I) by adopting a novel data perturbation mechanism. EPTD-I not only protects users’ privacy but also introduces little overhead on the user side. Moreover, for high mobility environments, we improve the design with a user non-interactive scheme named EPTD-II to shift all encrypted truth discovery operations to cloud platforms. In EPTD-II, each user’s sensitive information is also kept private during the complete truth discovery procedure. Thorough security analysis demonstrates that our proposed schemes are secure and offer a high level of privacy preservation. Extensive experiments conducted on practical and simulated crowdsensing applications demonstrate the effectiveness and efficiency of the proposed schemes.

中文翻译：

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人群感知应用中一种有效且隐私保护的真相发现方案

摘要 真相发现是解决移动人群感知系统中异构数据冲突和估计用户可靠性的一种可靠而有效的技术。尽管它很有效，但真相发现的广泛采用需要对用户的感官数据和可靠性信息进行可靠的隐私保护。现有的私人真相发现工作主要基于传统的加密原语，这给系统带来了巨大的工作量。在这项工作中，我们首先通过采用新颖的数据扰动机制提出了一种高效且保护隐私的真相发现框架（EPTD-I）。EPTD-I 不仅保护了用户的隐私，而且在用户端引入的开销很小。此外，对于高机动性环境，我们使用名为 EPTD-II 的用户非交互方案改进设计，将所有加密的真相发现操作转移到云平台。在 EPTD-II 中，每个用户的敏感信息在完整的真相发现过程中也是保密的。彻底的安全分析表明，我们提出的方案是安全的，并提供高水平的隐私保护。在实际和模拟人群感知应用程序上进行的大量实验证明了所提出方案的有效性和效率。