# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 2 Analysis of Modern-Day Smart Services
### Location-Based Services  
_Location-based services_ (_LBS_) are an application domain in which large amounts of data have to be processed. An LBS is a service in which the geographic location of entities is significantly tied in. In addition to tracking stationary entities, e.g., certain locations that are relevant for the service, mobile entities can also be tracked, e.g., shipments that are in delivery [21]. In addition to such non-human entities, the location data of human users are also being tracked by LBS. This is driven in particular by the increased use of smartphones (and affiliated technologies, such as smartwatches) [22]. This kind of device is not only permanently close to its user but also _always on_ and _always online_[23]. In addition to a GPS receiver, which enables very accurate positioning, mobile phone tracking also facilitates sufficiently good positioning of users via their GSM cellular location [24]. While satellite-based tracking is limited to outdoor areas, indoor tracking is also possible with standard smart devices. For instance, inertial approaches use built-in accelerometers and gyroscopes to determine the location relative to a given starting point by means of the direction of movement and movement speed. Other approaches are based on the Earth's magnetic field. A built-in magnetic sensor detects the radiated fields, which can be used to determine the current position by means of triangulation [25]. Basically, three different types of location capturing can be found in LBS. The most basic method for static entities is to hardcode their position. For mobile entities, the current location can be captured as singular location information with the help of the aforementioned tracking technologies. If several such singular locations are captured in a temporal sequence, they can be used to form trajectories that describe the movement path of an entity [26]. The latter in particular requires thorough data refinement, e.g., to eliminate outliers [27] or to compensate for inaccuracies in the location information [28]. Figure 1 illustrates three data refinement steps. A problem regarding the capturing of trajectories is that they are only recorded pointwise. Single points can be distorted due to poor positioning signals, e.g., when an entity is passing urban canyons. If the recorded location deviates strongly from the actual location, noise filtering can be used to rectify the  
trajectories. An outlier can be detected if a point of the trajectory deviates too much from its predecessor and successor. In that case, the corrupted point can be detected, deleted and, e.g., replaced by means of interpolation. If the sampling rate is higher, i.e., if more data are available for data refinement, the results of the cleansing become more accurate [29]. While noise filtering can be used to correct significant deviations from single points, smaller deviations can also be corrected by adding complementary knowledge. For instance, if an entity can only move on predefined paths, e.g., a parcel on a parcel conveyor or a car on a road, the trajectory can be projected onto a model of these possible paths. In the depicted map matching, the white paths are roads, and the entity is a car. The denoised trajectory can then be smoothed, since it can be assumed that the car follows the road [30]. As can be seen, however, such a smoothing has its limitations. For instance, it is not possible to determine where the trajectory ends. It is obvious that the car cannot drive off the road. However, the distance to the road above and the one below is identical. If further semantic knowledge is added to the model, even such errors can be corrected. For instance, if some locations are known that are relevant to the entity, then it can be inferred whether it is more likely to use the upper or lower road. In the shown semantic mapping, the green marker is the entity's place of residence. Thus, it can be assumed that the captured trajectory ends there, and the refinement of the trajectory can be made accordingly [31].