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A Literature Study on Privacy Patterns Research
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (SERG - SOFTWARE ENGINEERING)ORCID iD: 0000-0002-0107-2108
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (PRISEC - PRIVACY AND SECURITY)
Karlstad University, Faculty of Health, Science and Technology (starting 2013), Department of Mathematics and Computer Science. (SERG - SOFTWARE ENGINEERING)ORCID iD: 0000-0002-3180-9182
2017 (English)In: SEAA 2017 - 43rd Euromicro Conference Series on Software Engineering and Advanced Applications, IEEE, 2017Conference paper, Published paper (Refereed)
Abstract [en]

Context: Facing the implementation of the EU General Data Protection Regulation in May 2018, many commercial software providers will soon need to adapt their products to new privacy-related constraints. Privacy patterns defined for different aspects of the software engineering process promise to be a useful concept for this task. In this situation, it seems valuable to characterize the state of the research related to privacy patterns.Objective: To identify, characterize and classify the contributions made by published research results related to patterns in the context of considering privacy concerns in engineering software. Method: A literature review in form of a mapping study of scientific articles was performed. The resulting map structures the relevant body of work into multiple dimensions, illustrating research focuses and gaps.Results: Results show that empirical evidence in this field is scarce and that holistic approaches to engineering privacy into software based on patterns are lacking. This potentially hinders industrial adoption.Conclusion: Based on these results, we recommend to empirically validate existing privacy patterns, to consolidate them in pattern catalogues and languages, and to move towards seamless approaches from engineering privacy requirements to implementation.

Place, publisher, year, edition, pages
IEEE, 2017.
Keyword [en]
privacy patterns, privacy, software engineering, mapping study
National Category
Computer Science
Research subject
Computer Science
Identifiers
URN: urn:nbn:se:kau:diva-65025DOI: 10.1109/SEAA.2017.28ISBN: 978-1-5386-2141-7 (electronic)ISBN: 978-1-5386-2142-4 (print)OAI: oai:DiVA.org:kau-65025DiVA: diva2:1154009
Conference
2017 43rd Euromicro Conference on Software Engineering and Advanced Applications (SEAA) Aug 30 - Sept 1. Vienna, Austria
Available from: 2017-11-01 Created: 2017-11-01 Last updated: 2017-11-01

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Lenhard, JörgFritsch, LotharHerold, Sebastian
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CiteExportLink to record
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