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Architectural Optimization for Confidentiality Under Structural Uncertainty

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Book cover Software Architecture (ECSA 2021)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13365))

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Abstract

More and more connected systems gather and exchange data. This allows building smarter, more efficient and overall better systems. However, the exchange of data also leads to questions regarding the confidentiality of these systems. Design notions such as Security by Design or Privacy by Design help to build secure and confidential systems by considering confidentiality already at the design-time. During the design-time, different analyses can support the architect. However, essential properties that impact confidentiality, such as the deployment, might be unknown during the design-time, leading to structural uncertainty about the architecture and its confidentiality. Structural uncertainty in the software architecture represents unknown properties about the structure of the software architecture. This can be, for instance, the deployment or the actual implementation of a component. For handling this uncertainty, we combine a design space exploration and optimization approach with a dataflow-based confidentiality analysis. This helps to estimate the confidentiality of an architecture under structural uncertainty. We evaluated our approach on four application examples. The results indicate a high accuracy regarding the found confidentiality violations.

This work was supported by the German Research Foundation (DFG) under project number 432576552, HE8596/1-1 (FluidTrust), as well as by funding from the topic Engineering Secure Systems (46.23.03) of the Helmholtz Association (HGF) and by KASTEL Security Research Labs. Additionally, it was supported by the Czech Science Foundation project 20-24814J, and also partially supported by Charles University institutional funding SVV 260451.

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Notes

  1. 1.

    https://github.com/FluidTrust/Palladio-Addons-DataFlowConfidentiality-DSE.

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Acknowledgement

We like to thank Oliver Liu, who helped in developing this approach during his Bachelor thesis.

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Authors and Affiliations

  1. KASTEL – Institute of Information Security and Dependability, Karlsruhe Institute of Technology (KIT), Karlsruhe, Germany

    Maximilian Walter, Sebastian Hahner, Stephan Seifermann & Robert Heinrich

  2. Charles University, Prague, Czech Republic

    Tomas Bures, Petr Hnetynka & Jan Pacovský

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  1. Maximilian Walter
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  2. Sebastian Hahner
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  3. Stephan Seifermann
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  7. Robert Heinrich
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Correspondence to Maximilian Walter .

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Editors and Affiliations

  1. Università di Bergamo, Dalmine, Italy

    Patrizia Scandurra

  2. University of Canterbury, Christchurch, New Zealand

    Matthias Galster

  3. Politecnico di Milano, Milan, Italy

    Raffaela Mirandola

  4. KU Leuven, Kortrijk, Belgium

    Danny Weyns

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Walter, M. et al. (2022). Architectural Optimization for Confidentiality Under Structural Uncertainty. In: Scandurra, P., Galster, M., Mirandola, R., Weyns, D. (eds) Software Architecture. ECSA 2021. Lecture Notes in Computer Science, vol 13365. Springer, Cham. https://doi.org/10.1007/978-3-031-15116-3_14

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