Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects

Christoph Stach (), Clémentine Gritti, Julia Bräcker, Michael Behringer and Bernhard Mitschang
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Christoph Stach: Institute for Parallel and Distributed Systems, University of Stuttgart, Universitätsstraße 38, 70569 Stuttgart, Germany
Clémentine Gritti: Department of Computer Science and Software Engineering, University of Canterbury, Christchurch 8041, New Zealand
Julia Bräcker: Institute of Biochemistry and Technical Biochemistry, University of Stuttgart, Allmandring 5B, 70569 Stuttgart, Germany
Michael Behringer: Institute for Parallel and Distributed Systems, University of Stuttgart, Universitätsstraße 38, 70569 Stuttgart, Germany
Bernhard Mitschang: Institute for Parallel and Distributed Systems, University of Stuttgart, Universitätsstraße 38, 70569 Stuttgart, Germany

Future Internet, 2022, vol. 14, issue 11, 1-43

Abstract: The present information age is characterized by an ever-increasing digitalization. Smart devices quantify our entire lives. These collected data provide the foundation for data-driven services called smart services. They are able to adapt to a given context and thus tailor their functionalities to the user’s needs. It is therefore not surprising that their main resource, namely data, is nowadays a valuable commodity that can also be traded. However, this trend does not only have positive sides, as the gathered data reveal a lot of information about various data subjects. To prevent uncontrolled insights into private or confidential matters, data protection laws restrict the processing of sensitive data. One key factor in this regard is user-friendly privacy mechanisms. In this paper, we therefore assess current state-of-the-art privacy mechanisms. To this end, we initially identify forms of data processing applied by smart services. We then discuss privacy mechanisms suited for these use cases. Our findings reveal that current state-of-the-art privacy mechanisms provide good protection in principle, but there is no compelling one-size-fits-all privacy approach. This leads to further questions regarding the practicality of these mechanisms, which we present in the form of seven thought-provoking propositions.

Keywords: smart service; privacy techniques; location-based services; health services; voice-controlled digital assistants; image analysis; food analysis; recommender systems; DNA sequence classification (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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