Architectural Data Flow Analysis

In our connected world, ensuring the confidentiality of the software systems we build becomes increasingly difficult. Model-based design time confidentiality analyses have been proposed to cope with this complexity early. However, the usefulness of such analyses is limited due to uncertainty about the software architecture itself and the software's execution environment. This leads to conclusions about confidentiality violations that lack both precision and comprehensiveness. Although there exist approaches to deal with design time uncertainty, existing research lacks precise statements about the impact of uncertainty on confidentiality. To address this, we include uncertainty as part of our software architectural model. We extend a data flow-based analysis to include the impact of uncertainty on confidentiality violations. The results of the case study-based evaluation show high accuracy with typical design time uncertainty. Also, our analysis yields more precise statements about the impact of uncertainty on confidentiality than the state of the art.

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“…-5.) can be assumed as this information belongs to the requirements and the design phase of software systems [36], [38]. Uncertainty sources (6.…”

Section: Evaluation Results and Discussionmentioning

confidence: 99%

“…Still, they do not consider uncertainty and thus lack precision and comprehensiveness [15]. This becomes visible in over-estimations and also in missing violations [36].…”

Section: B Confidentiality Analysis and Uncertainty Awarenessmentioning

confidence: 99%

Model-based Confidentiality Analysis under Uncertainty

Hahner

Bitschi

Walter

et al. 2023

2023 IEEE 20th International Conference on Software Architecture Companion (ICSA-C)

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“…Data-Driven Palladio supplies a metamodel, which defines how data is represented and what kind of operations can be performed on data. Each data can have sets of characteristics, which represent abstract meta-data of the affiliated data [6]. The process, as shown in Fig.…”

Section: Foundationmentioning

confidence: 99%

Context-Based Confidentiality Analysis for Industrial IoT

Boltz

Walter

Heinrich

2020

2020 46th Euromicro Conference on Software Engineering and Advanced Applications (SEAA)

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In this research paper, we present an approach for an analysis process, which can find confidentiality issues in data-exchange, on the architectural level of Industrial Internet of Things software systems. Existing approaches provide an insufficient definition of dataflow or lack support of finely granulated information for providing confidentiality. Based on an existing modeling and analysis process for Data-Driven Software Architecture, we extend the role-based approach for access control, with a model to model transformation utilizing a contextbased approach. Using a case study based evaluation we show that our approach works accurately and scales in a way that is feasible for big organizations.

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“…Usage models describe how users use the system by calling offered services. A data flow modeling extension [18] allows specifying data processing in more detail. PCM subsystems allow representing multiple connected systems and their data interchange.…”

Section: Models Of Data Flowsmentioning

confidence: 99%

Modeling of dynamic trust contracts for industry 4.0 systems

Al-Ali

Heinrich

Hnětynka

et al. 2018

Proceedings of the 12th European Conference on Software Architecture: Companion Proceedings

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Due to their close relation to physical and virtual entities (humans, machines, processes, etc.) including their changing state and context, modern cyber-physical and IoT systems exhibit a high degree of architectural dynamicity. While sharing of data among all the entities of the system is the key driver to the efficiency of the system, it is at the same time necessary to effectively control which data are shared, with whom, and in which context so as to prevent potential misuse. The problem however is that traditional methods to security and privacy, which typically rely on rigid hierarchies, cannot easily cope with the high degree of architectural dynamicity. In this paper, we outline an approach to ensure security and privacy on the architectural level in systems with dynamic architectures.In particular, we focus on a) data tracking using data flows and data processing described in system architectures, b) descriptions of dynamic sharing scenarios including decision derivation based on the current situation, and c) a runtime analysis platform that regulates data exchange. We ground the approach and illustrate it in the Industry 4.0 setting, as this is the domain in which we apply our approach as part of our project Trust 4.0, but we believe it can be used in other applications domains as well. CCS CONCEPTS• Applied computing → Supply chain management; • Security and privacy → Domain-specific security and privacy architectures;

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Architectural Data Flow Analysis

Cited by 8 publications

References 3 publications

Model-based Confidentiality Analysis under Uncertainty

Model-based Confidentiality Analysis under Uncertainty

Context-Based Confidentiality Analysis for Industrial IoT

Modeling of dynamic trust contracts for industry 4.0 systems

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