# Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects
## 6 Conclusions  
In the modern information age, we are accustomed to smart services facilitating our everyday lives. We use these digital assistants in public, industrial, and private domains. Such data-driven services are so handy, as they adapt their behavior based on the current context and thus always provide an optimized user experience. However, this convenience does not come without a price. Smart services rely on permanent access to a vast amount of data. They analyze these data comprehensively to derive knowledge about the current situation of their users. Yet, this often involves highly personal or confidential data, allowing data processors to obtain sensitive information. For this reason, there are a variety of privacy measures that conceal certain sensitive knowledge patterns in the data without impairing the quality of the data. That is, they try to protect the privacy of data subjects and at the same time maintain the utility of their data for the respective smart services. For this reason, this paper addresses the question of whether state-of-the-art privacy mechanisms are prepared to meet this challenge. To this end, we carry out a SWOT-like assessment, in which we initially analyze the seven most relevant application scenarios for smart services. It is evident that users can benefit from these smart services in all situations of life. Yet, smart services also pose a high privacy threat due to the personal data they process. Although this statement generally applies to all smart services, it becomes apparent that the different application scenarios involve heterogeneous types of data. There is therefore no universal privacy threat but rather smart services-specific threats. Therefore, we also study the strengths and weaknesses of privacy approaches tailored to smart services. By comparing these two dimensions (opportunities and privacy threats of smart services on the one side and strengths and weaknesses of privacy approaches on the other side), we discover that there is an effective answer to every relevant privacy threat. However, there are fundamental problems with modern privacy technologies for smart services. Users are overwhelmed by the selection and configuration of privacy techniques. These techniques are always tailored to a specific use case, which is why a great deal of domain knowledge is required to apply them effectively. Otherwise, their protective effect is insufficient, and the negative impact on data quality is too high. The latter can lead to further security vulnerabilities if the data tampering is not target-oriented. Therefore, a trusted third party is often required for the application of the privacy techniques. From these findings, we derive pertinent open research questions and give our opinion on how they can be overcome. These research questions deal in particular with concepts for privacy requirements elicitation, a holistic privacy platform, the deployment of privacy filters, the configuration of privacy filters, a secure data management, proofs of data possession, and prevention of misinformation. Only when these issues are fully addressed is a privacy-by-design approach for smart devices feasible. Conceptualization, C.S. ; methodology, C.S. ; software, C.S. ; validation, C.S. ; formal analysis, 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. The following abbreviations are used in this paper: