## 1 Introduction  
Over the last fifteen years, the emergence of the Internet has led to changes in all areas of everyday life, affecting the lives of ordinary citizens, and within the next decade, the Internet of Things (IoT) revolution will affect the energy, agriculture and transport sectors, as well as the more traditional sectors of the economy and society. In this spirit, we are faced with a phenomenon of great economic and social potential that presents great opportunities but also important challenges associated with unimportant risks, of multidimensional and horizontal nature, which affect businesses and consumers, administrations and citizens alike [1]. For example, the rapid increase in the number of devices capable of collecting personal data from the user's environment is one of the most basic and most serious modern forms of threat to the users' privacy. Participating devices in the IoT ecosystem share information that can be used to track activities and permanently record the status of users and their characteristics or activities. The data in question can be used to make user profiles, with or without their agreement, and enable automated decision-making for third parties [2]. Following the enforcement of General Data Protection Regulation (GDPR), consumers have enhanced their ability to control their personal data and personal preferences, as they can control how the data produced by their IoT devices are used, and to whom and why access to these data is provided [3]. Thus, the legal framework safeguards the absolute right of users to privacy and the protection of their personal data, while providing rights to information access and to be forgotten. However, there are no avoidable breaches related to profiling, with a view to fully automated decision-making for data subjects. The GDPR on Article 4(4) says that profiling is [4]: \"_Any form of automated processing of personal data consisting of the use of personal data to evaluate certain personal aspects relating to a natural person, in particular to analyze or predict aspects concerning that natural person's performance at work, economic situation, health, personal preferences, interests, reliability, behavior, location or movements_\". Specifically, organizations gain personal information about entities from a variety of different sources, such as internet searches, buying behaviors, lifestyle, and activities. This data can be gathered from mobile phones, social networks, video surveillance systems, and the IoT applications and can be analyzed to classify persons into diverse groups that links different characteristics to create profiles for individuals. Although the majority of Internet users are particularly worried about the exposure of their personal data, since they consider their privacy to be an important issue and want to have control over their personal data, most face threats related to them passively, perhaps even sluggishly. The main reason is their inability to understand the means and techniques used for collecting their personal data, how these data are being used, and most importantly, they are unaware of ways to take active measures to effectively protect their privacy. In the recent past, several researchers pointed out that users are usually unaware of profiling and tracking activities, which may be carried out by malicious stakeholders even when the user relies on (seemingly) anonymous procedures [5]. For example, profiling issues can be handled well based on privacy-related methods, such as k-anonymity [6] or pseudo-anonymity [7] in the privacy preservation of published data. Another key aspect involving privacy is that, often, data features multiple individuals or, more specifically, data that seems to involve only certain individuals, in fact, reveal information about others [8]. For instance, in [9], the authors introduced the problem of collaborative privacy on the social networks and provide photo-sharing as a case study. Also, [PERSON] et al.