# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 3 State-of-the-Art Privacy Measures
### Key Findings  
Our key findings regarding the means of privacy protection as well as the effects of the aforementioned privacy techniques are summarized in Table 2.  
It is evident that there are dedicated privacy filters for different types of data, namely for location data, time-series data, voice data, and image data. Furthermore, there are techniques to conceal private patterns in a stream of data items. Thereby, it is possible to filter sensitive information in a fine-grained manner. Using these and similar obfuscation techniques, the \(\varepsilon\)-differentially private properties can also be established, which ensures that statistical computations are performed on a plethora of personal datasets--including the extraction of information patterns that are the foundation of knowledge--without exposing the privacy of any individual. Figure 15: A Distributed Training of a Global Knowledge Model via Federated Learning. However, these dedicated privacy techniques can only be applied systematically if a certain underlying structure is present in the data. For generic raw data, only general privacy measures can be applied. These include, e.g., the three operators of relational algebra mentioned initially: namely, selection, projection, and aggregation. The dedicated privacy filters are therefore significantly better suited for backend systems after comprehensive data refinement measures have been carried out, and processable information has already been retrieved. Meanwhile, on lightweight smart devices, which are more likely to handle raw data, the general privacy measures should be preferred as they are easier to apply. Yet, these general measures have to be used purposefully, as they are not capable of filtering private aspects specifically and thus tend to have a more significant impact on data quality. Federated learning represents a completely different approach. Here, data preprocessing carried out in a distributed manner under the supervision of the data subjects. Only highly aggregated information in the form of machine learning models is forwarded to the backend system. In this way, each data subject can decide independently which privacy measures are applied locally to the data. However, data subjects often lack the necessary knowledge to do this in a systematic manner that is tailored to the intended purpose of the models. Moreover, federated learning is only suited for very certain machine learning algorithms and not for general purpose data refinement. In this context, it is arguable whether it is even necessary to apply privacy measures locally on the smart devices of the users. A local application of privacy filters is primarily reasonable if the data processor to which these smart devices forward the collected data is not trustworthy. If this is not the case, it is more advisable to apply privacy measures on the side of the data processor. Such a global approach can be more targeted and results in stronger privacy protection for the same scope of data filtering--i.e., the trade-off between privacy and utility is significantly better [167]. Data processors in the service domain are commonly assumed to be _semi-honest-but-curious_. That is, they largely carry out their tasks in a trustworthy manner, and they will not deliberately expose the privacy of a user [168].