Understanding user privacy in Internet of Things environments

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.