## 3 Related Work
### Data Privacy  
Due to the Quantified Self movement, i.e., the continuous self-tracking with IoT technology, the amount of personal data is growing exponentially [149]. The analysis of such data is also of high economic value for the industry, as it reveals a lot about potential customers [150]. Therefore, one aspect of data security is becoming increasingly relevant in the context of data processing and management, namely data privacy. The idea of data privacy originates from philosophical theories that predate the information age. These theories reflect the basic need of humans to keep certain information about themselves secret. Historical examples include the Hippocratic Oath [151], the seal of confession [152], and the secrecy of correspondence [153]. In these examples, the owner of the secret trusts that the person keeping the secret will adhere to his duty of professional secrecy [154]. In a broader sense, however, data privacy is motivated by the fundamental human desire not to be at the mercy of external control and to have autonomy over one's personal data [155]. A pioneering work that attempts to define this philosophical idea describes privacy as follows: \"Privacy is the claim of individuals, groups, or institutions to determine for themselves when, how, and to what extent information about them is communicated to others\" [156]. To this end, two aspects are particularly noteworthy: On the one hand, it is evident that privacy, unlike the protection goals discussed in Section 3.2, is an individual right--that is, each data subject must be able to decide individually what information s/he wants to shares with society. On the other hand, this definition implies a necessity to provide controlling and regulating measures for the sharing of personal information [157]. Therefore, in our digitalized world, it is particularly important that laws comprehensively preserve the privacy of all individuals. This includes a plethora of organizational, technical, and legal measures necessary to enable data subjects to enforce these two aspects in the information society of today [158]. Data protection laws such as the General Data Protection Regulation (GDPR) [159] therefore demand technical measures that give data subjects full control over their data. However, the so-called privacy paradox arises here--although data subjects have fundamental privacy concerns, they do not want to give up the comfort and respective benefit they experience from the analysis of their data. In the context of the IoT and its smart services, this problem is even more aggravated [160]. For effective data protection, it is therefore not only important to protect private data, but also to maintain the perceived quality of service. Otherwise, the privacy measures would be rejected by users. Technical privacy measures can be divided into three categories: measures that inform data subjects about potential privacy risks, measures that restrict the sharing and processing of personal data, and measures that erase data permanently [161]. Only with such technical measures it is possible to comply with the requirement for data protection by design and default [162]. Besides these technical measures, data protection laws also deal with organizational measures (e.g., the appointment of a dedicated contact person for data subjects or the obligation that data must not leave a predefined territorial scope) [163]. In the context of this work, however, we are focusing on technical challenges, only. Right to be Informed.Data protection regulations give data subjects the right to be informed about the processing of their data. Three main research directions can be identified in this context. First, techniques are being developed to explain what knowledge can be derived from certain types of data. For instance, IoT data sources are described with the help of ontologies. Thus, it can be disclosed which data is collected by a sensor (including, e.g., the accuracy and frequency) and which information is derived from the respective data [164].