## 4 Scenarios and Data  
Smart entertainment devices greatly upgrade users' home entertainment experience by giving them full control over all the devices of a home ecosystem through voice, applications, and remote control. At the same time, privacy concerns are constantly increasing, arising from the fully accessible and interconnected home environment of smart devices, which produce or record an increasing number of data. These concerns should also be recorded in cases of data collection without the user's full awareness or consent but also by the ignorance of the consequences of their sharing. In order to clearly identify and implement the proposed IPAM, the problem of recording user data from smart entertainment devices was simulated. After a thorough study of the operation of these devices, bibliographic review and analysis of their manuals, the following variables, which can be collected by smart entertainment devices, were chosen to model the problem of profiling:  
**User Identity Data**  
1. Username. The username of the user. 2. Sex. The sex of the user. 3. Age. The age of the user. **Location Data**  
4. Location_History. If the machine caches the location history of its usage. **Status Data**  
5. Device_Number. The unique address of the device (mac address). 6. Device_Type. Device type, for example, TV. 7. IP_Address. The IP address of the device. 8. Operating System. The device's OS. **Actionable Data**  
9. Automation. If the device starts/stops automatically. 10. Average Time. The average time that the device is being used during a day. 11. Average Duration. The average duration of using an application on the device, e.g., Youtube or Netflix. 12. Interests. If the user registers his/her interest on a service, e.g., action movies on Netflix. **Class**  
13. Yes or No. Automated individual decision-making, including profiling. All of the above variables are binary data, i.e., either collected (1) or not (0). Table 1 shows some cases of the parameters that make up the problem. In fact, the above table depicts the symbolic description and representation of the problem configuration described, which considers the combination of the cases that may arise from the recording or not of the corresponding characteristics. Specifically, and given that our problem contains 12 parameters (username, sex, age, location_history, device_type, device_type, IP, OS, automation, average_time, average_duration, interests) that can receive two possible states, the total dataset includes \(2^{12}\) possible situations, i.e., 4096 cases. Before proceeding to the problem modeling methodology described in Section 5, it should be noted that the implementation of the scenarios was based solely on a heuristic, approximate method of analysis. The scenario of the 12 specific parameters with two possible states implements a model that is an abstract representation of the real system. This denotes that it implements only certain properties and characteristics of the real system, without taking into account all of them. The need for selection arises from the fact that real scenarios are extremely complex and cannot be fully represented. Therefore, the model simplifies reality by choosing a part of it which is suitable for:  
1. Giving a general rather than a generalized view of the problem. 2. Focusing on the parameters of how smart entertainment devices work, which is one of the objectives of this research. 3. Creating a technically robust dataset, which can, without serious disadvantages, properly train learning algorithms, while correspondingly being characterized as a relatively high complexity set. 4. Capturing a clear, relatively detailed, and practically possible picture of an unconventional security problem for which no technical details, methodology references, and experimental data are available. It should be noted that there are exponentially multiple and different cases of variables that can be plotted as system parameters and give a different view of the problem.