Runtime Model-Based Privacy Checks of Big Data Cloud Services

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9435)

Abstract

Cloud services have to comply with privacy policies when storing or processing data. As cloud services become increasingly data-intensive, e.g., in the case of big data analytics, data privacy concerns become more critical and challenging to address. In particular, data may only be processed at certain geo-locations. However, the actual geo-locations of the many storage and compute nodes involved in big data processing is dynamically selected during runtime. In addition, the execution of concrete data processing tasks may change data classifications from, e.g., personal to anonymized data. Thus, privacy policy checks for big data cloud services have to consider information about the actual nodes and data processing tasks at runtime. The proposed approach R-PRIS monitors cloud services to derive and maintain typed runtime models providing the aforementioned information. R-PRIS checks the typed runtime models against privacy policies by employing a data-classification-aware search. The evaluation of R-PRIS, performed on Amazon Web Services (including Hadoop), indicates that the approach may efficiently and timely detect privacy violations in big data cloud services.

Keywords

Privacy Big data Cloud services Runtime checking 

Notes

Acknowledgements

This work was partially supported by the DFG (German Res. Found.) under Priority Programme “SPP1593” (grant PO 607/3-1).

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Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  1. 1.paluno (The Ruhr Institute for Software Technology)University of Duisburg-EssenEssenGermany

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