## 5 Assessment
### Security and Privacy Assessment
No custom-made approaches for availability and authenticity are introduced in the REFINERY Platform. Rather, established techniques are also used for this purpose. For instance, the raw data are managed using HDFS. This distributed file system ensures high availability, as the data are redundantly distributed on different nodes. The relational databases in the Information Zone also have a high fault tolerance and allow recovering the stored data in the unlikely event of a failure. Thus, this also applies to the data products in the Use Case Zones, as they can be rebuilt based on the data from the Raw Data Zone and the Information Zone via the ontology. For IoT devices (i.e., the data sources), permanent availability cannot be achieved. However, our synchronization mechanism for the secured data store ensures that in case of connection failure, all new data are transmitted to the REFINERY Platform as soon as the connection is re-established. The digital signatures ensure the authenticity of the data as they certify the origin of the data. We have not addressed issues related to the authenticity of data consumers. For us, it is only important that the REFINERY platform is able to identify them. There are numerous mature approaches such as attribute-based credentials that can be used for this purpose. Authentication of data consumers is not within the scope of our work, as it has no implications for the REFINERY Platform. Our access policy approach describes the insights that can be gained from certain data by mapping the rather abstract raw data to human-comprehensible knowledge patterns. This illustrates to data producers what can be extracted from their data. Furthermore, the ontology reflects the full data processing workflow. In this way, we enable data subjects to obtain detailed information about the processing of their data in a way that even non-IT experts can understand. Thus, our REFINERY Platform inherently implements the right to be informed. In addition, due to the PET which are geared to the privacy requirements, e.g., privacy filters or statistical disclosure control, we also offer technical support to implement the right to restriction of processing. This is particularly effective in our approach for two reasons: On the one hand, we use a variety of dedicated privacy filters and techniques. Thus, certain information contents in the data can be specifically concealed without degrading the overall data quality. On the other hand, we deploy the privacy filters in both the user-controlled area (i.e., on the edge devices in the form of the PMP) and in the REFINERY Platform mass data storage (i.e., in the Data Preparation Zone and in the Privacy Zone). This distribution allows us to apply the privacy filter in a much more targeted manner. It also leverages the strengths of both approaches. As a result, the usability of the data can be maintained without having to make any sacrifices in terms of privacy. Our access policy model also contributes to demand-driven data provisioning, as data accesses are mapped to actual purposes instead of a smart service as a whole. The introduction of activation contexts enables an even more fine-grained permission management. This reduces the privacy paradox since users are no longer faced with a more or less binary choice between privacy and service quality. We have not taken any explicit measures to enforce the right to be forgotten. Our concept is based on the assumption that the REFINERY Platform is operated by a trusted party. If a data subject therefore makes use of the right to be forgotten, we provide technical support for this, e.g., our ontology to easily identify and subsequently delete all data products related to the raw data in question. Explicit means for data subjects to verify that the REFINERY Platform actually deletes all data in question are not necessary given our basic assumption. Table 1 summarizes the key data security and data privacy concepts integrated into our REFINERY Platform for the four data administration tasks.