## 2 Background
### Impact of Filtering on Performance and Data Quality
Also, the original work [26] indicates that Bark provides its functionality with a reasonably acceptable overhead in most use cases. Their experiments indicate 'that, for a reasonable number of applicable rules (<10), the additional latency is unnoticeable to users' [26]. However, the original work does not include bandwidth measurements. Overall, the above findings suggest that filtering on the level of network packets (Filtering here operates at _level 3_, also called the _network layer_, of the OSI layer model [37] and allows that filter rules can be based on IP addresses and ports), even with non-optimised rule-parsing and non-optimised rules, e.g., 'latency increases as the number of rules that are statically matched and fruitlessly evaluated increases' [26], can be sufficiently fast. Successful filtering, i.e., blocking a network packet and its contained data from reaching its destination, can result in reduced data transmission and thus in a reduction of the data quality. Reducing data quality slightly is a common privacy protecting mechanism and can counter problems like that too fine-grained energy values allow detecting the use or mode of use of electrical appliances within a Smart Home [51; 52]. Depending on the application's need in terms of data quality, the application's functionality could be able to tolerate a certain amount of blocked packets or totally cease to be useful. For example, assume the application in which a constant video stream of a video camera is sent to an Internet server in order to alert the user when motion is detected and being able to receive a recording of the event that triggered the warning on the user's Smartphone. However, capturing the hallway during normal times could be forbidden by a rule in order to preserve the privacy of the people in the Smart Home, but could be enabled for dates on which the user is away on vacation to be alerted of burglars or other events. Overall, filtering can reduce the data quality and it is out of the scope of this paper to discuss it in more detail. In this paper we provide generalised usage scenarios and their dataflow (see Section 5), the impact of filtering on privacy and security has been measured without any compromise of the functionality enabled by the indicated data flows. For example, if the communication with an outside server is in the dataflow for that scenario, then the assumption is that filtering preserves that and hence the functionality should be fully preserved. If one wants to balance privacy concerns and a minimum level of data quality then this would require mechanisms allowing to set maximum limits on the amount of data quality decrease, e.g., comparable to [36]. If-even further-a partial loss of functionality could be tolerated by the user, then a playful and dynamic way to interact with the rules could enable the user to directly see the consequences of a rule on  
Figure 5: Performance evaluation of our own Bark-style prototypical filtering setup tested on a Raspberry PI 4B. The x axis corresponds to the number of network devices \(\in[50,100,300,500,700]\). Apart from the baseline value, three graphs are shown, representing the number of checks that are needed to evaluate the tested set of filtering rules \(\in\{50,150,250\}\). the functionality, e.g., as suggested in [47]. For example, a user that is blocking Internet communication of the smart lights using filtering would notice that this removes remote accessibility to smart lights via an application on a Smartphone when outside the home's WLAN but might notice that it still retains the Smartphone application's functions while the Smartphone is in the local WLAN.