## 3 Related Work  
Although our research focuses on solving privacy problems in Smart Homes through filtering, this topic is quite broad and can be addressed from different perspectives and using different methods. Since it is impossible to mention every work in this domain, in this section, we discuss only a few works that we believe are the most relevant to our study. In [53; 54], a risk analysis model for a Smart Home Automation System called _SHAS_ is presented. (The two papers by the same authors likely present results of the same risk analysis. While the results are slightly different, the discrepancy can be explained by the fact that in the former, the risk values were rounded before categorisation.) While the model focuses on ZigBee and Z-Wave devices, which can also be controlled remotely over a dedicated cloud service, the scenarios that were considered throughout this paper consider an IP-based Smart Home, where all sorts of devices can in principle communicate directly with each other. As a result, the authors identify 32 risks with repeating vulnerability and threat descriptions. Similar work was carried out in [55], where [PERSON] et al. discuss a handful of rather abstract security and privacy threats, namely Eavesdropping, Denial of Service, Impersonation and Software exploitation, for which they discuss potential attack vectors. [PERSON] et al. present a comprehensive threat taxonomy [56] that focuses on the Smart Home domain. Here, threats are considered along three dimensions, with the first one focusing on different communication protocols, the second on security and privacy impacts on the system, and the last one on the impact on physical symptoms and psychological reactions of the home residents. Together, they provide a wide overview of vulnerabilities that were identified in the literature. In order to enhance the security and privacy of Smart Home systems and mitigate threats and vulnerabilities discovered in previously mentioned works, numerous different approaches and techniques have been developed. In our work, we discuss a few of them that are closest to our main focus. In [57], [PERSON] et al. propose the use of a white worm, called _AntibIcTic_, which invades vulnerable devices and then removes existing malware and closes vulnerabilities such as unnecessary open ports or default passwords. Additionally, it adds network filtering at a central node, which either grants or completely prohibits Internet access for a device, depending on the configured operation mode of the network as well as depending on the success of invading and securing the device. In [58], while mainly concerned with the security and privacy issues of Hybrid Broadcasting Broadband TV (HbbTV), [PERSON] and [PERSON] also discuss the use of a firewall for Smart Homes in general, emphasising the importance of a usable GUI for the acceptance of such an approach. They notice that technical details such as IP addresses and port numbers should be hidden from the user. In [59], [PERSON] et al. propose and implement a gateway for Smart Homes that incorporate sensors and actuators that use the KNX building automation standard. The gateway transports information to external endpoints and allows remote control using the Session Initiation Protocol (SIP). During research, the authors noticed the need for different roles, such as guests. They propose using a three-layer security architecture consisting of encryption and two access control lists in order to limit the device and status information access. [PERSON] et al. specify a network filtering-based approach, called _IDIoT_[24], which restricts traffic leaving the network based on policies. They discuss three options for obtaining such policies, namely the manufacturer or third parties or by observing network traffic, an idea which has also been proposed by [43; 60]. The policies allow specifying network endpoints, communication limits, and application-specific restrictions, e.g., regarding DNS requests and responses.