## 3 Related Work
### Data Privacy
The obfuscation techniques for users apply privacy filters to the data. Very simple filters allow horizontal filtering [175]--which corresponds to the selection operator in relational algebra, i.e., filtering out specific data items from a dataset--or vertical filtering [176]--which corresponds to the projection operator in relational algebra, i.e., filtering out specific features of a data item. Also, an aggregation, i.e., condensing many data items to a single representative data item (e.g., the mean), can be used as a privacy filter [177]. However, such generic privacy filters are rather coarse-grained and therefore severely impair the data quality. Privacy filters that are tailored to a specific type of data are therefore more appropriate, as they are fine-grained and thus less invasive [178]. For instance, there are privacy filters that are tailored to location data and can optionally obfuscate individual locations or entire trajectories [179], privacy filters for health data that enable certain types of examinations only, while rendering the data unusable for all other analyses [180], or privacy filters that mask the voice or remove revealing background noise in speech data [181]. Other approaches focus on complex events that represent a specific sequence of data items instead of individual data items. Privacy-critical events are concealed, e.g., by reordering the data items or dropping or inserting data items. This preserves privacy without reducing the data quality of the data themselves [182]. Statistical disclosure controls for mass data providers include methods in which individual data subjects are hidden in the faceless masses formed by all users [183]. For instance, \(k\)-anonymity approaches ensure that a data item can only be mapped to a group of \(k\) users. With a sufficiently large \(k\), no private information can be derived from the data about each of the \(k\) individuals [184]. Differential privacy is intended to protect an individual even more extensively. It ensures, e.g., by adding noise, that statistical analyses cannot determine whether the data of an individual data subject contributed (significantly) to the outcome [185]. While this sounds tempting in theory, differential privacy often turns out to be complex to implement and very costly to compute in practice [186]. If parameterized inappropriately, it even offers little protection and therefore leads to a false sense of security [187]. Federated learning can be regarded as an approach in the field of distribution of data across multiple trusted third parties. Here, the data with which a machine learning model is to be trained is distributed among several parties. Each party calculates its own local model, which is then incorporated into a global model. By partitioning the data appropriately, it can be ensured that no party gains a comprehensive insight into the data [188]. By using privacy filters when creating the local models, it can also be ensured that no unintended conclusions can be drawn from the global model [189]. Right to be Forgotten.The right to be informed and the right to restriction of processing are, however, not sufficient to provide all-round protection. As data can become outdated, the privacy requirements of data subjects can change, or data may have been unlawfully transferred to data providers in the first place, there is also a need for a right to have all personal data erased. This also includes all related metadata as well as all data products in which these data have been integrated. In addition, personal data also have an inherent lifespan, after which they must also be completely deleted, as they may not be stored longer than required for the intended purpose. In terms of today's data protection regulations, data erasure has to ensure that the data cannot be restored in any way or form [190]. From a technical point of view, therefore, measures are needed to enable secure deletion of all the data concerned.