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Privacy-Preserving Cloud Computing for Biomedical Research

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Abstract

Applications in the biomedical sector have a rising demand for computational power due to the rapid growth of biological data. However, the setup and maintenance of a sufficient computational infrastructure is costly. Cloud computing allows distributed computing over a network and maximizes the efficiency of the shared resources. Nevertheless, security concerns rise when valuable research data is transferred to a public Cloud. Herein, we describe—from the application and security perspective—three biomedical case studies from different domains: Patent annotation, cancer outcome prediction, and drug target prediction. We elaborate on different strategies to secure the data and results in the Cloud as well as on the infrastructure needed. The proposed Cloud solutions can help to adapt similar algorithms from different domains to benefit from Cloud computing.

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Authors and Affiliations

  1. TU Dresden, Institute of Systems Architecture, Dresden, Germany

    Martin Beck

  2. TU Dresden, BIOTEC, Dresden, Germany

    V. Joachim Haupt, Janine Roy, Jan Moennich, Michael Schroeder & Zerrin Isik

  3. TU Dresden, Center for Information Services and High Performance Computing (ZIH), Dresden, Germany

    René Jäkel

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  1. Martin Beck
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  2. V. Joachim Haupt
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  3. Janine Roy
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  4. Jan Moennich
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  5. René Jäkel
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  7. Zerrin Isik
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Correspondence to Martin Beck .

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Editors and Affiliations

  1. Technische Universität München, fortiss - An-Institut Technische Universität München, Munich, Germany

    Helmut Krcmar

  2. Karlsruhe Institute of Technology, FZI - Forschungszentrum Informatik am KIT, Karlsruhe, Germany

    Ralf Reussner

  3. RWTH Aachen, RIT - Rumpe Information Technologies, Aachen, Germany

    Bernhard Rumpe

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Beck, M. et al. (2014). Privacy-Preserving Cloud Computing for Biomedical Research. In: Krcmar, H., Reussner, R., Rumpe, B. (eds) Trusted Cloud Computing. Springer, Cham. https://doi.org/10.1007/978-3-319-12718-7_16

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  • DOI: https://doi.org/10.1007/978-3-319-12718-7_16

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