ARTSense: Anonymous reputation and trust in participatory sensing
ARTSense: Anonymous reputation and trust in participatory sensing
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DOI：
10.1109/infcom.2013.6567058
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发表时间：
2013-04
期刊：
影响因子：
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通讯作者：
Xinlei Wang;W. Cheng;P. Mohapatra;T. Abdelzaher
中科院分区：
文献类型：
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作者：
Xinlei Wang;W. Cheng;P. Mohapatra;T. Abdelzaher
随着嵌入式传感器移动计算设备的激增，参与式传感越来越流行，用于从参与用户收集信息并将任务外包给参与用户。这些应用程序处理大量个人信息，例如用户的身份和特定时间的位置。因此，我们需要更加关注隐私和匿名。然而，从数据消费者的角度来看，我们希望知道感知数据的来源，即发送者的身份，以便评估数据的可信度。 “匿名”和“信任”是参与式传感网络中两个相互冲突的目标，目前还没有研究同时实现这两个目标的可能性。在本文中，我们提出了 ARTSense，一个解决参与式感知网络中“无身份信任”问题的框架。我们的解决方案由隐私保护来源模型、数据信任评估方案和匿名声誉管理协议组成。我们已经证明 ARTSense 实现了匿名性和安全性要求。进行验证是为了表明我们可以准确捕获参与者的信息信任度和声誉。
With the proliferation of sensor-embedded mobile computing devices, participatory sensing is becoming popular to collect information from and outsource tasks to participating users. These applications deal with a lot of personal information, e.g., users' identities and locations at a specific time. Therefore, we need to pay a deeper attention to privacy and anonymity. However, from a data consumer's point of view, we want to know the source of the sensing data, i.e., the identity of the sender, in order to evaluate how much the data can be trusted. “Anonymity” and “trust” are two conflicting objectives in participatory sensing networks, and there are no existing research efforts which investigated the possibility of achieving both of them at the same time. In this paper, we propose ARTSense, a framework to solve the problem of “trust without identity” in participatory sensing networks. Our solution consists of a privacy-preserving provenance model, a data trust assessment scheme and an anonymous reputation management protocol. We have shown that ARTSense achieves the anonymity and security requirements. Validations are done to show that we can capture the trust of information and reputation of participants accurately.