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Situativer Datenschutz im Fog-Computing

  • HAUPTBEITRAG
  • SITUATIVER DATENSCHUTZ IM FOG-COMPUTING
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Informatik Spektrum Aims and scope

Zusammenfassung

Fog-Computing erlaubt, Software-Code oder Daten dynamisch von ressourcenschwachen Endgeräten an leistungsstärkere Geräte am Rande des Netzwerks und in der Cloud auszulagern. Eine solche dynamische Auslagerung ermöglicht eine performante Ausführung rechenintensiver Aufgaben, bei gleichzeitig geringer Latenzzeit für die Datenübertragung. Beim Datenschutz ergeben sich im Fog-Computing jedoch spezifische Herausforderungen. Wir beschreiben die wesentlichen Herausforderungen des Datenschutzes im Fog-Computing und diskutieren, wie diese Herausforderungen durch die situative Kombination verschiedener Datenschutztechniken zur Laufzeit adressiert werden können.

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References

  1. Alrawais A, Alhothaily A, Hu C, Cheng X (2017) Fog computing for the Internet of Things: Security and privacy issues. IEEE Intern Comput March/April 2017:34–42

  2. Aral A, Brandic I (2017) Quality of Service channelling for latency sensitive edge applications. IEEE International Conference on Edge Computing, pp 166–173

  3. Ardagna CA, Asal R, Damiani E, Vu QH (2015) From security to assurance in the cloud: A survey. ACM Computing Surveys 48(1):art 2

  4. Costan V, Lebedev IA, Devadas S (2016) Sanctum: Minimal hardware extensions for strong software isolation. USENIX Security Symposium, pp 857–874

  5. Di Nitto E, Ghezzi C, Metzger A, Papazoglou M, Pohl K (2008) A journey to highly dynamic, self-adaptive service-based applications. Autom Softw Engin 15(3–4):313–341

    Article  Google Scholar 

  6. He T, Ciftcioglu EN, Wang S, Chan KS (2017) Location privacy in mobile edge clouds: A chaff-based approach. IEEE J Sel Areas Commun 35(11):2625–2636

    Article  Google Scholar 

  7. Heinrich R, Jung R, Schmieders E, Metzger A, Hasselbring W, Reussner R, Pohl K (2015) Architectural run-time models for operator-in-the-loop adaptation of cloud applications. In: IEEE 9th International Symposium on the Maintenance and Evolution of Service-Oriented and Cloud-Based Environments, pp 36–40

  8. Kephart JO, Chess DM (2003) The vision of autonomic computing. Computer 36(1):41–50

    Article  MathSciNet  Google Scholar 

  9. Luthra M, Koldehofe B, Weisenburger P, Salvaneschi G (2018) TCEP: Adapting to dynamic user environment by enabling transitions between operator placement mechanisms. In: 12th ACM International Conference on Distributed and Event-based Systems, pp 136–147

  10. Mann ZÁ (2016) Multicore-aware virtual machine placement in cloud data centers. IEEE T Comput 65(11):3357–3369

    Article  MathSciNet  Google Scholar 

  11. Mann ZÁ, Metzger A (2017) Optimized cloud deployment of multi-tenant software considering data protection concerns. In: 17th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, pp 609–618

  12. Mann ZÁ, Metzger A (2018) The special case of data protection and self-adaptation. In: ACM/IEEE 13th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, pp 190–191

  13. Mann ZÁ, Metzger A, Schoenen S (2018) Towards a run-time model for data protection in the cloud. In: Schaefer I, Karagiannis D, Vogelsang A, Méndez D, Seidl C (Hrsg) Modellierung 2018. Gesellschaft für Informatik e. V., pp 71–86

  14. Mattern F, Floerkemeier C (2010) Vom Internet der Computer zum Internet der Dinge. Informatik-Spektrum 33(2):107–121

    Article  Google Scholar 

  15. Orsini G, Bade D, Lamersdorf W (2016) CloudAware: A context-adaptive middleware for mobile edge and cloud computing applications. In: IEEE International Workshops on Foundations and Applications of Self* Systems, pp 216–221

  16. Palm A, Mann ZÁ, Metzger A (2018) Modeling data protection vulnerabilities of cloud systems using risk patterns. In: 10th System Analysis and Modeling Conference, pp 1–19

  17. Riaz Z, Dürr F, Rothermel K (2016) On the privacy of frequently visited user locations. In: 17th IEEE International Conference on Mobile Data Management, vol 1, pp 282–291

  18. Richerzhagen B, Koldehofe B, Steinmetz R (2015) Immense dynamism. German Research 2/2015, Wiley VCH, pp 24–27

  19. Salehie M, Tahvildari L (2009) Self-adaptive software: Landscape and research challenges. ACM Trans Auton Adapt Sys 4(2):art 14

  20. Schmieders E, Metzger A, Pohl K (2015) Runtime model-based privacy checks of big data cloud services. In: International Conference on Service-Oriented Computing, pp 71–86

  21. Schoenen S, Mann ZÁ, Metzger A (2018) Using risk patterns to identify violations of data protection policies in cloud systems. In: Braubach L et al (eds) Service-Oriented Computing – ICSOC 2017 Workshops. LNCS vol 10797, pp 296–307

  22. Skarlat O, Schulte S, Borkowski M, Leitner P (2016) Resource provisioning for IoT services in the fog. In: IEEE 9th International Conference on Service-Oriented Computing and Applications, pp 32–39

  23. Stojmenovic I, Wen S (2014) The fog computing paradigm: Scenarios and security issues. In: Federated Conference on Computer Science and Information Systems, pp 1–8

  24. Tietz V, Blichmann G, Hübsch G (2011) Cloud-Entwicklungsmethoden. Informatik-Spektrum 34(4):345–354

    Article  Google Scholar 

  25. van Dijk M, Gentry C, Halevi S, Vaikuntanathan V (2010) Fully homomorphic encryption over the integers. In: Advances in Cryptology – EUROCRYPT 2010. LNCS vol 6110, pp 24–43

  26. Wang W, Hu Y, Chen L, Huang X, Sunar B (2015) Exploring the feasibility of fully homomorphic encryption. IEEE Trans Comput 64(3):698–706

    Article  MathSciNet  MATH  Google Scholar 

  27. Wang L, Jiao L, Li J, Mühlhäuser M (2017) Online resource allocation for arbitrary user mobility in distributed edge clouds. In: IEEE 37th International Conference on Distributed Computing Systems, pp 1281–1290

  28. Wrobel S, Voss H, Köhler J, Beyer U, Auer S (2015) Big data, big opportunities. Informatik-Spektrum 38(5):370–378

    Article  Google Scholar 

  29. Yi S, Li C, Li Q (2015) A survey of fog computing: Concepts, applications and issues. In: Workshop on Mobile Big Data, pp 37–42

  30. Yi S, Qin Z, Li Q (2015) Security and privacy issues of fog computing: A survey. In: International Conference on Wireless Algorithms, Systems, and Applications, pp 685–695

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Authors and Affiliations

  1. paluno, The Ruhr Institute for Software Technology, Universität Duisburg-Essen, Essen, Deutschland

    Zoltán Ádám Mann, Andreas Metzger & Klaus Pohl

Authors
  1. Zoltán Ádám Mann
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  2. Andreas Metzger
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  3. Klaus Pohl
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Correspondence to Zoltán Ádám Mann.

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Mann, Z., Metzger, A. & Pohl, K. Situativer Datenschutz im Fog-Computing. Informatik Spektrum 42, 236–243 (2019). https://doi.org/10.1007/s00287-019-01190-1

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  • DOI: https://doi.org/10.1007/s00287-019-01190-1

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