# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Health Services  
The healthcare sector can also benefit from comprehensive data analyses. The term _eHealth_ covers all kinds of services that facilitate the treatment and long-term care of patients and involve the use of modern information technologies [39]. This brings many benefits to the table, as treatment costs can be reduced, the patients' quality of life can be improved, and the workload of physicians can be reduced [40]. To achieve this, however, it is crucial to include the patients fully in the process and overcome technical hurdles [41]. This can be achieved in particular by means of so-called _mHealth_. Here, everyday mobile devices such as smartphones are used for medical treatment [42]. Due to mHealth, there is a mobile application for virtually any health-related use case nowadays [43]. An example of such an mHealth application for people suffering from a chronic disease is an interactive questionnaire with which they can assess their condition on a daily basis. Depending on the answers, follow-up questions are asked. In this way, only essential questions can be asked in a systematic manner without overwhelming patients [44]. In addition to a local evaluation to determine the appropriate question catalog, the results are also forwarded to a backend for further analysis. If a critical situation is detected based on the answers, the patient is advised to see a physician [45]. The physician receives a summary of the analyzed questionnaires, which facilitates his or her daily work and allows him or her to focus on emergency cases [46]. Such applications are especially useful in more rural areas or developing countries where people have no immediate access to a physician. Thus, because of the low-cost hardware required, mHealth can ensure that people in such areas still receive healthcare [47]. Today's smart devices, however, enable far more powerful heath services due to their built-in sensors and connectivity [48]. The so-called _quantified self movement_ motivates people to use self-tracking to capture vast amounts of health-related data about themselves, such as blood pressure information, on a permanent basis [49]. While ordinary smart devices come with many sensors that support this kind of self-tracking, the IoT opens up further capabilities [50]. For instance, a smartphone can be turned into a health data hub to which all sorts of IoT-enabled specialized heath meters send their measurement data for storage and processing [51]. As a result, mHealth approaches provide not only a sufficient amount of data but also useful data for more comprehensive medical analyses [52]. In addition to simple questionnaires, the self-assessment of patients can be supported by such health-related measurements. In the case of diabetics, a continuous glucose monitoring device can be coupled with a smartphone to track the blood glucose level in a mobile application. Moreover, external circumstances that might influence the measurements (e.g., stress factors such as noise) can also be captured via the smartphone's built-in sensors. In this way, analyses enable a 360-degree health view on the patients [53]. As these data are tracked continuously, more complex analyses of long-term trends are also feasible [54]. Figure 2 shows the potential of such analyses. First, _descriptive analytics_ can be applied to the large amounts of historical data collected about a patient. By looking at a health value over time, it is possible to understand what exactly has happened, e.g., how a patient responded to taking a particular pharmaceutical, and use this knowledge to adjust the medication. Yet, as the data are captured in real time, the current situation can also be monitored by means of _real-time analytics_. For instance, this knowledge can be used to operate a smart insulin pump that supplies a patient with the appropriate amount of insulin based on the current situation. In addition, a model can be trained using historical data, which enables _predictive analytics_.