Refining Privacy-Aware Data Flow Diagrams
Paper in proceeding, 2021

Privacy, like security, is a non-functional property, yet most software design tools are focused on functional aspects, using for instance Data Flow Diagrams (DFDs). In previous work, a conceptual model was introduced where DFDs were extended into so-called Privacy-Aware Data Flow Diagrams (PA-DFDs) with the aim of adding specific privacy checks to existing DFDs. An implementation to add such automatic checks has also been developed. In this paper, we define the notion of refinement for both DFDs and PA-DFDs as a special type of structure-preserving map (or graph homomorphism). We also provide three algorithms to find, check and transform refinements, and we show that the standard diagram "transform→refine/refine→transform" commutes. We have implemented our algorithms in a proof-of-concept tool called DFD Refinery, and have applied it to realistic scenarios.

Dataflow diagrams



Privacy by Design


Hanaa Alshareef

Chalmers, Computer Science and Engineering (Chalmers), Formal methods

Sandro Stucki

Chalmers, Computer Science and Engineering (Chalmers), Information Security

Gerardo Schneider

University of Gothenburg

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13085 121-140
9783030921231 (ISBN)

19th International Conference on Software Engineering and Formal Methods, SEFM 2021
Virtual event, ,

Perspex: Flexible and Transparent Local Differential Privacy

Swedish Research Council (VR) (2018-04230), 2019-01-01 -- 2022-12-31.

Areas of Advance

Information and Communication Technology

Subject Categories

Information Science

Computer Science

Computer Systems



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