## 3 Related Work
### Data Privacy
Based on such ontologies, reasoning can be done about what knowledge can be derived from this information [165]. These ontologies can be regarded as persisted knowledge of technical experts and domain experts. By involving privacy experts, the ontology can be extended in order to identify potential privacy threats and thereby make data subjects aware of critical issues. From this information, abnormal and questionable data usage patterns can be detected [166]. Second, in addition to informing about correlations between data and knowledge, research approaches also aim to describe the actual data processing. For this purpose, a holistic view of data-driven applications and processes is provided in order to identify inherent privacy threats. In the field of system safety, the STAMP framework (STAMP stands for System-Theoretic Accident Model and Processes) provides such a top-down approach. Here, causal relationships between hazards and actions are initially modeled. Based on this model, the System-Theoretic Process Analysis (STPA) can be performed to identify problems in the design of the system that lead to or facilitate these hazardous actions [167]. This approach can be adapted to the field of privacy threats. Here, potential privacy threats posed by a data-processing application have to be identified initially, e.g., using the ontologies described earlier. The components of the application are then analyzed to determine whether they are sufficiently protected against these privacy threats [168]. Such a top-down analysis is ideal for modeling and analyzing complex data-driven systems and informing data subjects about inherent privacy-related vulnerabilities with respect to the data sources involved [169]. Third, data subjects also have a right to be informed about the data products for which their data was used as input. Machine learning models represent a prime example. Although these models can be processed by machines easily, they are usually a black box for humans. When applying such a model, it is generally nontransparent why it came to a certain result (e.g., a prediction or a suggestion). Since the models may be flawed or unfair due to an insufficient amount of appropriate training data, it is crucial that the results (and thus the models themselves) are comprehensible to data subjects [170]. To this end, there are three research areas: In the field of interpretable models, methods are developed which generate explainable models. In the field of model induction, models, which represent a black box, are transformed into an explainable model. Yet, both approaches have limitations when it comes to deep learning, as models in this context are far more complex. In deep explanation, deep learning algorithms are therefore adapted in such a way that not the model but at least the relevance of individual input factors are identified across the layers of the model, in order to determine what eventually led to a decision [171]. Yet, there are still many open questions to be solved, especially in the area of deep learning, before full transparency is achieved [172]. Right to Restriction of Processing.However, all this information is of little use to a data subject in exercising his or her digital self-determination if s/he cannot also object to the data processing and enforce this technically. To this end, however, a binary consent system is far too restrictive. Here, a rejection leads to a massive reduction in service quality, which tempts data subjects to agree to all requests. Therefore, this is not an actual informed consent [173]. Instead, privacy-enhancing technologies (PET) should be used to minimize the data--or rather the amount of information they contain--in a target-oriented manner in accordance with the privacy requirements of the data subjects. Three fundamentally different types of PET can be identified: obfuscation techniques for users, statistical disclosure control for mass data providers, and distribution of data across multiple trusted third parties [174].