Understanding user privacy in Internet of Things environments
Understanding user privacy in Internet of Things environments
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DOI：
10.1109/wf-iot.2016.7845392
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发表时间：
2016-12
期刊：
影响因子：
--
通讯作者：
Hosub Lee;A. Kobsa
中科院分区：
文献类型：
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作者：
Hosub Lee;A. Kobsa
在过去的十年中，用户隐私已经成为网络计算环境中的一个重要问题。例如，移动的应用和设备越来越多地要求用户提供个人信息，以及通过行为跟踪来监视用户。在即将到来的物联网（IoT）时代，这种侵犯隐私的做法可能会随着传感器设备的激增而增加。然而，到目前为止，旨在了解人们与物联网相关的隐私概念的研究相对较少。在早期的工作中，我们公布了五个表征物联网服务场景的上下文参数，以及五个描述人们对场景态度的反应参数。在本文中，我们的目标是了解这些上下文参数如何影响人们对物联网场景的隐私感知。为此，我们对200名受访者进行了一项关于2800个假设物联网场景（主要是关于信息监控活动）的调查，并使用K-modes聚类算法进行了分析。我们确定了四组场景，具有明显不同的相关用户反应。通过比较不同的集群，我们可以识别与物联网环境中传感器跟踪接受度较高或较低相关的上下文参数。
During the past decade, user privacy has become an important issue in networked computing environments. For instance, mobile applications and devices are increasingly asking users to provide personal information, as well as monitoring users through behavioral tracking. This privacy-invasive practice is likely to increase with the proliferation of sensor devices in the upcoming era of Internet of Things (IoT). However, there has been comparatively little research so far aimed at understanding people's notion of privacy in connection with IoT. In earlier work, we unveiled five contextual parameters that characterize IoT service scenarios, and five reaction parameters that describe people's attitudes toward the scenarios. In this paper, we aim to understand how these contextual parameters impact people's privacy perceptions of IoT scenarios. To this end, we conducted a survey with 200 respondents on 2800 hypothetical IoT scenarios (mostly about information monitoring activities), and analyzed them using a K-modes clustering algorithm. We identified four clusters of scenarios, with clearly distinctive associated user reactions. By comparing the different clusters, we can identify contextual parameters that are associated with higher or lower acceptance of sensor tracking in IoT environments.