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Data Trustworthiness—Approaches and Research Challenges

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Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance (DPM 2014, QASA 2014, SETOP 2014)

Part of the book series: Lecture Notes in Computer Science ((LNSC,volume 8872))

Abstract

With the increased need of data sharing among multiple organizations, such as government organizations, financial corporations, medical hospitals and academic institutions, it is critical to assess and assure data trustworthiness so that effective decisions can be made based on data. In this paper, we first discuss motivations and relevant techniques for data trustworthiness. We then present an architectural framework for a comprehensive system for trustworthiness assurance and discuss relevant recent work. We highlight open research issues and research directions throughout the paper.

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Acknowledgments

The work reported in this paper has been partially supported by the Purdue Cyber Center and the National Science Foundation under grant CNS-1111512.

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Authors and Affiliations

  1. Computer Science Department and Cyber Center, Purdue University, West Lafayette, IN, USA

    Elisa Bertino

Authors
  1. Elisa Bertino
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Corresponding author

Correspondence to Elisa Bertino .

Editor information

Editors and Affiliations

  1. Télécom SudParis, Evry, France

    Joaquin Garcia-Alfaro

  2. Universitat Autònoma de Barcelona, Bellaterra, Spain

    Jordi Herrera-JoancomartĂ­

  3. Imperial College London, London, United Kingdom

    Emil Lupu

  4. Universität Passau, Passau, Germany

    Joachim Posegga

  5. University of Urbino, Urbino, Italy

    Alessandro Aldini

  6. Pisa Research Area, National Research Council - CNR, Pisa, Italy

    Fabio Martinelli

  7. Technische Universität Darmstadt, Darmstadt, Germany

    Neeraj Suri

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Bertino, E. (2015). Data Trustworthiness—Approaches and Research Challenges. In: Garcia-Alfaro, J., et al. Data Privacy Management, Autonomous Spontaneous Security, and Security Assurance. DPM QASA SETOP 2014 2014 2014. Lecture Notes in Computer Science(), vol 8872. Springer, Cham. https://doi.org/10.1007/978-3-319-17016-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-17016-9_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17015-2

  • Online ISBN: 978-3-319-17016-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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