Non-Interactive Privacy-Preserving Truth Discovery in Crowd Sensing Applications
Non-Interactive Privacy-Preserving Truth Discovery in Crowd Sensing Applications
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DOI：
10.1109/infocom.2018.8486371
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发表时间：
2018-04
期刊：
影响因子：
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通讯作者：
Xiaoting Tang;Cong Wang-;Xingliang Yuan;Qian Wang
中科院分区：
文献类型：
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作者：
Xiaoting Tang;Cong Wang-;Xingliang Yuan;Qian Wang
在群体感知中，真相发现 (TD) 是指从不同提供商收集的嘈杂/有偏见的数据中找到可靠的信息。为了在实现真相蒸馏的同时保护提供商的数据，保护隐私的真相发现（PPTD）最近受到了广泛关注。然而，所有现有方法都需要服务器和各个提供商之间的迭代交互，这不可避免地要求所有提供商始终在线。否则，协议将失败或暴露额外的提供者信息。在本文中，我们设计并实现了第一个非交互式 PPTD 系统，该系统完全消除了在线要求，并具有强大的隐私保证。我们的框架遵循与先前最著名的解决方案相同的双服务器模型，并利用 Yao 的乱码电路 (GC)。然而，我们为 TD 优化实施设计了重要的加速技术。首先，我们确定了 TD 中的可重用计算，以加速电路生成。其次，我们通过定制的近似来安全地评估 TD 中繁重的非线性函数，准确度更高，效率更高。第三，我们通过将基于组件的 GC 的最新进展和 TD 所需的各种计算结合在一起，减少了在线执行时间。与现有技术不同，我们的框架不揭示任何中间结果，并且进一步支持“晚加入”提供商而无需协议暂停/重启。我们的概念验证实施的实际性能通过广泛的评估得到验证。
In crowd sensing, truth discovery (TD) refers to finding reliable information from noisy/biased data collected from different providers. To protect providers' data while enabling truth distillation, privacy-preserving truth discovery (PPTD) has received wide attention recently. However, all existing approaches require iterative interaction between server(s) and individual providers, which inevitably demand all providers to be always online. Otherwise, the protocol would fail or expose extra provider information. In this paper, we design and implement the first non-interactive PPTD system that completely removes the online requirement with strong privacy guarantees. Our framework follows the same two-server model from the best-known prior solution, and leverages Yao's Garbled Circuit (GC). Yet, we devise non-trivial speedup techniques for TD-optimized implementation. Firstly, we identify reusable computations in TD to accelerate the circuit generation. Secondly, we securely evaluate the burdensome non-linear functions in TD via customized approximation with accuracy and improved efficiency. Thirdly, we reduce the online execution time by bridging together latest advancements of component-based GC and various computations needed in TD. Unlike prior arts, our framework does not reveal any intermediate results, and further supports “late-join” providers without protocol suspension/restart. The practical performance of our proof-of-concept implementation is verified through extensive evaluations.