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Workshop on Secure Data Management

SDM 2013: Secure Data Management pp 37–41Cite as

Data Security and Privacy in 2025?

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Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8425))

Abstract

Security research aims at reducing the risk and consequences of attacks on information technology. Based on the projection of current trends, this vision paper makes an attempt at identifying potential security research challenges for the next 10 years. Examples of identified challenges are the trend to have pervasive computing in tiny devices, to collect and analyze data from these devices and other sources, and to increase the connection between IT and physical systems.

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Notes

  1. 1.

    E.g. [11] describes surveillance cameras where persons are replaced by anonymised shapes at the source.

  2. 2.

    Assume that each device requires 1 s of human intervention, then 1B of devices require 32 person-years.

  3. 3.

    For instance, in case of a fault (e.g., caused by an attack) in an automotive control system, the breaks may still slow down and stop the car while there is no fail-safe mode for the steering.

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Acknowledgements

This position paper solely reflects a subjective opinion of the author. Nevertheless, it was formed based on valuable input by my team at the Intel Collaborative Research Center for Secure Computing and at Intel Labs. In particular Christian Wachsmann of TU Darmstadt provided feedback on drafts of this position paper.

Author information

Authors and Affiliations

  1. Intel Labs, Darmstadt, Germany

    Matthias Schunter

Authors
  1. Matthias Schunter
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Correspondence to Matthias Schunter .

Editor information

Editors and Affiliations

  1. EIT ICT Labs/University of Twente, Enschede, The Netherlands

    Willem Jonker

  2. Philips Research/Eindhoven University of Technology, Eindhoven, The Netherlands

    Milan Petković

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© 2014 Springer International Publishing Switzerland

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Schunter, M. (2014). Data Security and Privacy in 2025?. In: Jonker, W., Petković, M. (eds) Secure Data Management. SDM 2013. Lecture Notes in Computer Science(), vol 8425. Springer, Cham. https://doi.org/10.1007/978-3-319-06811-4_8

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  • DOI: https://doi.org/10.1007/978-3-319-06811-4_8

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-06810-7

  • Online ISBN: 978-3-319-06811-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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