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

Author

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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)

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.

Suggested Citation

  • Christoph Stach & Clémentine Gritti & Julia Bräcker & Michael Behringer & Bernhard Mitschang, 2022. "Protecting Sensitive Data in the Information Age: State of the Art and Future Prospects," Future Internet, MDPI, vol. 14(11), pages 1-43, October.
    • Handle: RePEc:gam:jftint:v:14:y:2022:i:11:p:302-:d:950323
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    References listed on IDEAS

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    1. Burim Ramosaj & Markus Pauly, 2019. "Predicting missing values: a comparative study on non-parametric approaches for imputation," Computational Statistics, Springer, vol. 34(4), pages 1741-1764, December.
    2. Shanshan Guo & Xitong Guo & Xiaofei Zhang & Doug Vogel, 2018. "Doctor–patient relationship strength’s impact in an online healthcare community," Information Technology for Development, Taylor & Francis Journals, vol. 24(2), pages 279-300, April.
    3. Mohsen Pourahmadi, 1989. "Estimation And Interpolation Of Missing Values Of A Stationary Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 10(2), pages 149-169, March.
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    Cited by:

    1. Christoph Stach, 2023. "Data Is the New Oil–Sort of: A View on Why This Comparison Is Misleading and Its Implications for Modern Data Administration," Future Internet, MDPI, vol. 15(2), pages 1-49, February.
    2. Christoph Stach & Clémentine Gritti, 2023. "Special Issue on Security and Privacy in Blockchains and the IoT Volume II," Future Internet, MDPI, vol. 15(8), pages 1-7, August.

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