# SMARTEN--A Sample-Based Approach towards Privacy-Friendly Data Refinement
## 3 Related Work
When enriching data, randomly generated noise is usually added to the actual data in order to reduce the accuracy of the data--and thus the amount of information contained [49]. However, such a one-size-fits-all approach is not always effective. On the one hand, generic noise may not sufficiently mask certain confidential information in some cases, or it may be removed very easily [50]. On the other hand, such noise can be too intrusive and render the data unusable in the process [51]. Therefore, it is better to rely on privacy filters that are tailored to the particular data and their intended processing [52]. For instance, there are specialized approaches for location data [53], health data [54], time series data [55], voice data [56], and image data [57]. Such approaches ensure that the synthetically generated data appear plausible in the context of the data subject [58]. In addition to such filters targeting specific data sources, there are also higher-level filters for _events_, i.e., specific combinations of data from different sources [59]. Hide in the Masses.While the technical measures to conceal sensitive information described above primarily targeted data from individual users, the protection of such data can also be seen as a community task. If many individuals are willing to expose their data to a certain risk of disclosure, sensitive information can also be hidden in the masses of data. This principle can be explained very simply by means of an example: When General [PERSON] tried to arrest [PERSON], many followers of [PERSON] claimed to be him. This way, the real [PERSON] blurred with the masses and could no longer be identified as an individual. The more people thereby expose themselves to the danger of being arrested, the lower is the actual danger for each individual [60]. This fundamental idea also applies to the protection of confidential data. Often, the data themselves are not particularly sensitive as long as they cannot be linked to a data subject. Thus, instead of tampering with the data and thus degrading the quality of the data, it is often sufficient to pool the data of many individuals, so that a single record cannot be uniquely linked to any of them. It is only possible to tell that they belong to one of the \(k\) individuals that participate. In this way, it is possible to perform better analyses on the data than it would have been possible if arbitrarily filters are applied to the data of each individual [61]. [PERSON] [62] introduces _differential privacy_, a measure that can be used to assess the risk to an individual who hides his or her data in the masses. To this end, an analysis is performed on the data of all participating individuals. The analysis results are then compared to the results of the same analysis on a _neighboring database_, i.e., base data without the data of the individual in question. If the two results differ only by a small \(e\), the data of that individual are not exposed. However, it has to be mentioned that this result cannot be directly applied to all other individuals or to all types of analyses because the level of exposure may differ in each case. While there is a lot of research work on how to ensure these \(e\)-differentially private properties [63], which show promising results [64], it still has rather little significance in practice due to the high complexity to apply such measures [65]. Synopsis.When looking at the related work, only the horizontal and vertical withholding of data can be applied to arbitrary raw data. However, in the context of data refinement, these measures are often too restrictive in terms of reducing not only the amount of sensitive information contained in the data but also the quality of data and thus their value. The dedicated filtering techniques can operate at a much higher level of granularity and thus better preserve data quality. To this end, however, the data must be available in the appropriate form and are afterwards only usable for their intended purpose.