Consumer-Friendly Methods for Privacy Protection Against Cleaning Robots

As Internet of Things (IoT) technologies enter the consumer market, smart cleaning robots have gained high attention and usage in households. However, consumers behave differently even if many claim they are concerned about smart robot privacy issues. In this paper, we attempt to discover effective measures for average consumers to guard against potential privacy intrusions brought by cleaning robots. We defined our target devices, provided an ideal smart home network topology and established our threat model. We documented network redirection and analytic methods we used during our research. We categorized existing privacy protection methods and described their general procedures. We assessed and evaluated the protection methods against three aspects: protection effectiveness, functionality loss and consumer-friendliness. In the end we performed a tabular qualitative comparison and expressed our vision towards privacy protection against cleaning robots.