# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures  
In this section, we study which technical measures can be used to conceal information in order to comply with privacy requirements. In this context, it is important to reduce the information content in line with processing requirements, i.e., the knowledge required by a data consumer must still be derivable from the data in an adequate quality. If this is not feasible, the technical measures are not applicable in practice--this would be equivalent to withholding the data. In this context, it is first of all necessary to understand which data must be protected at all in order to be able to apply the privacy measures in a target-oriented manner and to minimize the impact on data quality. The GDPR stipulates that only _personal data_ have to be protected. These are data that can be unambiguously traced to a date subject (Article 4(1)). Without such a linkage, no special privacy measures are required. However, most smart services across all domains require an authentication of their users. For instance, it must be possible to link measured values to the correct user, or it must be ensured that a user is authorized to execute certain commands. As part of this authentication, users are identified. This can be avoided by outsourcing the authentication to a trusted third party. This results in a segregation of the identification data and the payload data. The trusted third party only forwards pseudonymized subsets of the identifying attributes to the smart service provider [110]. As a result, the smart service provider cannot directly link the data to a natural person but only to a pseudonymous entity. Yet, if one has access to both the identification data and the payload data, these data can be joined again via the pseudonymized references. From the perspective of the GDPR, these are therefore identifiable entities, i.e., the personal data can be indirectly linked to a natural person. Such data must be protected in the same way as data which can be directly linked to a natural person. Thus, privacy measures are required by law for almost all smart services. However, even with (apparently) fully anonymized data, there is a risk to the privacy of users, which is why additional privacy measures should be taken in any case [111]. Generally speaking, there are three approaches to share data in a privacy-friendly way. First, the base data can be minimized by removing entire data items based on certain properties prior to processing [112]. For instance, in the health care sector, a dataset of patient records can be reduced by removing all medical records of a certain patient in order to preserve the privacy of that patient. In formalized terms, this is the application of a _selection operator_\(\sigma\) known from relational algebra. It is defined as:  
\[\sigma_{\varphi}(R)=\{t:t\in R,\varphi(t)\} \tag{1}\]  
Let \(R\) be a relation and \(\varphi\) be a propositional formula describing which tuples are to be included in the result set, i.e., the tuples \(t_{i}\) for which \(\varphi(t_{i})\) evaluates to true. \begin{table}
\begin{tabular}{p{56.9 pt} p{142.3 pt} p{142.3 pt}} \hline \hline
**Application** & **Required Data Processing** & **Privacy or Confidentiality Concerns** \\ \hline Food Analysis & Patterns indicating, e.g., allergens must be detected in food samples. & Other patterns reveal secret ingredients, thereby disclosing trade secrets. \\ Recommender Systems & Large amounts of data from many individuals must be analyzed to make appropriate recommendations. & Although the trained models do not disclose information about individuals, the underlying data do. \\ DNA Sequence Classification & Neural networks have to be trained based on a comprehensive DNA database to detect new correlations. & DNA data contain sensitive information; hence, third parties must not have full access to the complete dataset. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._Second, certain attributes of the data items can be excluded from processing [113].