# SMARTEN--A Sample-Based Approach towards Privacy-Friendly Data Refinement
## 6 Conclusions  
Due to technological progress, it is nowadays possible to capture data on literally any aspect of life almost continuously. Smart services take advantage of this fact by preparing the acquired data in order to extract processable information from them. This information can then be processed and analyzed to derive knowledge. This knowledge is the key driver of smart services. Users benefit significantly from smart services as the comprehensive knowledge base enables them to tailor their portfolio of services to the user's needs and thus offer him or her an optimal service experience. Despite the undeniable advantages of such smart services, which have become an indispensable part of everyday life, this development also has its negative aspects. When data are prepared and processed, the parties involved also gain deep insights into confidential and privacy-sensitive information. It is therefore mandatory to take technical measures which ensure that smart services are provided with the necessary data, but prevent the disclosure of sensitive data. To this end, we discuss the refinement process that transforms raw data into operational knowledge. While there are various approaches to conceal sensitive information, all of these approaches mainly deal with the second phase of the data refinement process, i.e., the data processing phase. For the data preparation phase, there are no dedicated privacy approaches--let alone a holistic consideration of the data refinement process from a privacy point of view. We therefore introduce SMARTEN, a privacy approach that smartens up services in a privacy-friendly manner. To achieve this goal, we make the following three contributions:  
1. By adapting the data preparation phase of the data refinement process, data stewards only obtain access to a representative sample of the data. The samples are compiled in such a way that they comply with given privacy constraints. A data steward operates on the sample, and these data wrangling activities can be transferred to pre-processing rules, which are then applied to the entire data stock. This way, data can be transformed into processable information without providing too many insights. 2. By adapting the data processing phase of the data refinement process, different privacy filters can be applied to the acquired information before it is analyzed in order to derive knowledge. The privacy filters are designed for each type of data in such a way that specific aspects of the data can be concealed without impairing the quality of the data significantly and thus rendering smart services inoperable in the process. 3. Using two-tier annotations, data owners can specify their privacy requirements. The outer tier specifies which requirements must be met in the data preparation phase while the inner layer describes which privacy measures have to be applied in the data processing phase. In SMARTEN, these annotations are realized as digital signatures that can be attached directly to the source data and thus inseparably tied to them. By means of these three contributions, SMARTEN renders the data refinement process privacy-friendly without unnecessarily restricting smart services. Evaluation results show that our sample-based data refinement approach is feasible for data stewards and achieves a satisfactory level of data quality. In addition, the data processing overhead is relatively low. Conceptualization, C.S. ; methodology, M.B. and C.S. ; software, M.B., C.G. and C.S. ; validation, M.B., C.G. and C.S. ; formal analysis, M.B. and C.S. ; investigation, C.S. ; resources, J.B., C.G. and B.M. ; data curation, M.B., J.B., C.G. and C.S. ; writing--original draft preparation, C.S. ; writing--review and editing, M.B., J.B., C.G., B.M. and C.S. ; visualization, C.S. ; supervision, C.S. ; project administration, C.S. ; funding acquisition, B.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. Not applicable. The authors declare no conflict of interest.