A Privacy-driven Enterprise Architecture Meta-Model for Supporting Compliance with the General Data Protection Regulation

Autor: Fabian Burmeister, Ingrid Schirmer, Paul Drews
Přispěvatelé: Bui, Tung X.
Rok vydání: 2019
Předmět:
Zdroj: HICSS
Burmeister, F, Drews, P & Schirmer, I 2019, A Privacy-driven Enterprise Architecture Meta-Model for Supporting Compliance with the General Data Protection Regulation . in T X Bui (ed.), Proceedings of the 52nd Annual Hawaii International Conference on System Sciences, HICSS 2019 . Proceedings of the Annual Hawaii International Conference on System Sciences, vol. 2019-January, University of Hawaiʻi at Mānoa, Honolulu, pp. 6052-6061, 52nd Annual Hawaii International Conference on System Sciences-HICSS 2019, Maui, Hawaii, United States, 08.01.19 . https://doi.org/10.24251/HICSS.2019.729
Burmeister, F, Drews, P & Schirmer, I 2019, A Privacy-driven Enterprise Architecture Meta-Model for Supporting Compliance with the General Data Protection Regulation . in Proceedings of the 52nd Hawaii International Conference on System Sciences . University of Hawaiʻi at Mānoa, Honolulu, pp. 6052-6061, Hawaii International Conference on System Sciences-HICSS 2019, Hawaii, United States, 08.01.19 .
ISSN: 2572-6862
DOI: 10.24251/hicss.2019.729
Popis: The processing of personal data has evolved into an integral component of businesses by providing several data-driven opportunities. Simultaneously, businesses struggle with the associated responsibility for privacy, as recent data scandals have shown. As a consequence, the European Commission has passed the General Data Protection Regulation (GDPR) to enhance the rights of citizens and the requirements on data protection. This paper argues that enterprise architecture (EA) models can be a key to compliance with the GDPR. Following an incremental research approach, we categorize the major obligations resulting from the GDPR, derive essential stakeholder concerns and outline necessary EA elements for capturing aspects of analytics, security and privacy in EA models. On this basis, a privacy-driven EA meta-model is developed that is capable of answering key concerns resulting from the GDPR.
Databáze: OpenAIRE
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