Privacy Compliance Via Model Transformations

Abstract

Due to the upcoming, more restrictive regulations (like the European GDPR), designing privacy preserving architectures for information systems is becoming a pressing concern for practitioners. In particular, verifying that a design is compliant with the regulations might be a challenging task for engineers. This work presents an approach based on model transformations, which guarantee that an architectural design encompasses regulation-oriented principles such as purpose limitation, or accountability of the data controller. Our work improves the state of the art along two main dimensions. The approach we propose (i) embeds privacy principles coming from regulations, thus helping to bridge the gap between the technical and the legal worlds, (ii) systematize the embedding of the privacy principles coming from regulations, thus enabling a constructive approach to privacy by design.

A Model-based Approach to Realize Privacy and Data Protection by Design

“…[24]- [26] have all defined GDPR conceptual models supplemented with compliance rules; while others [27], [28] have defined meta-models for privacy derived from access control paradigms. Yet others ( [29], [30]) have addressed the introduction of privacy concerns into process models by defining extensions to modeling languages (BPMN [12] and Data Flow Diagrams [13]), and their transformations based on privacy patterns. Indeed, Data Flow Diagrams have themselves been repeatedly employed to model data processing activities in the context of security and privacy engineering [31], [32].…”

Incorporating privacy by design in body sensor networks for medical applications: A privacy and data protection framework

“…Although authors support that the proposed patterns provide generally applicable privacy guidelines, it is important to note that their work focused only on the following specific GDPR principles, Transparency and Traceability, Purpose Limitation, Data Minimization, Accuracy and Storage Limitation, since the principles of Lawfulness, Fairness, Integrity and Confidentiality and Accountability are considered not to be fulfilled by technical measures in a manageable time limit. Authors in [36] presented an approach based on model transformations, aiming to enable a more constructive approach to privacy by design under the principles of GDPR. Although, their work consists an interesting approach to bridge privacy legal and technical field, it focuses only on limited requirements, such as purpose limitation, or accountability of the data controller and consequently it presents specific technical privacy properties.…”

Contextualisation of Data Flow Diagrams for Security Analysis

“…Antigac et al [1] examined how certain properties of a DFD can be hotspots for further investigation. For example, a usage hotspot corresponds with 3 DFD elements: data flow d into process p, process p, and data flow d from p. Antigac et al showed how such hotspots bridge the gap between different models, and provide a basis for subsequent model transformation without fundamentally changing the visual semantics of DFDs.…”