# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 5 Future Prospects
Therefore, privacy measures cannot be applied in a target-oriented manner, which means that they turn out to be either too restrictive or insufficient. Whereas, when applying privacy filters in the data backend, the data are no longer within the user's sphere of influence and the user must completely trust that his or her privacy requirements are respected by the backend, which represents a major psychological hurdle [181]. In simple terms, the privacy platform must therefore find a deployment plan for which a utility metric is maximized. At an abstract level, this metric looks like this:  
\[Utility=Data\ Quality+Privacy \tag{5}\]  
The utility of a deployment plan is defined by how well it preserves privacy and how little it impairs data quality. A high-level definition, analogous to the configuration based on knowledge patterns, could look like this:  
\[Utility=\sum_{i}Public_{i}*w_{Public_{i}}-\sum_{j}False_{j}*w_{False_{j}}-\sum_ {k}Private_{k}*w_{Private_{k}} \tag{6}\]  
Here, the data quality is described by how many public knowledge patterns--i.e., non-confidential knowledge patterns--can be detected despite the use of privacy filters (\(\sum_{i}Public_{i}\)), minus all _false positives_, i.e., public knowledge patterns that were falsely recognized due to the use of privacy filters (\(\sum_{j}False_{j}\)). The privacy of a deployment plan is determined by how few private knowledge patterns it exposes (\(\sum_{k}Private_{k}\)). Additionally, a penalty weight \(w\) can be assigned to each of these components to prioritize them differently depending on the specific use case. The research question here is how to efficiently determine the one with the best utility from all possible deployment plans. (e) Secure Data Management.Since the application of privacy filters can be time consuming, they should not be applied as data flow operators over and over for every data access. Instead, frequently used data should be stored in the data backend at different privacy levels, i.e., after different privacy filters have been applied. Zone-based data lakes are suitable for managing big data in different processing stages. In addition to filtered raw data, higher-value information, for instance in the form of machine learning models trained by means of federated learning, can also be stored in such data lakes [182]. For this, the data backend acts as a _data marketplace_. Similar to a marketplace for material goods, customers, e.g., smart services, can pick the data they want [183]. This requires extensive metadata that characterizes the available data so that smart services can also find relevant data [184]. In contrast to conventional marketplaces, however, privacy constraints must be observed in data marketplaces; i.e., not every customer may access every data item [185]. Moreover, since the existence of a data item exposes certain information, even the visibility of the items has to be regulated in this case. In this regard, it must be ensured that the operator of the data marketplace cannot abuse this. Since s/he has sovereignty over the data, s/he could maliciously withhold certain data items to the disadvantage of a data subject or a smart service. By using blockchain technologies this can be prevented, a trustworthy data sharing is enabled [186]. Yet, the use of a blockchain raises further privacy issues, e.g., due to its immutability or the fact that in a data marketplace, there are legitimate reasons why a certain party is not allowed to see a specific data item [187]. Future research therefore has to address how existing metadata models need to be extended to include aspects such as applied privacy measures in addition to data quality or data origin. Furthermore, access policies must be developed to ensure that certain data items are not visible to selected smart services. However, it is also important to ensure that these mechanisms cannot be misused to the disadvantage of data subjects or smart services.