Query-Driven Enforcement of Rule-Based Policies for Data-Privacy Compliance

Matschinske JO (2019)

Publication Language: English

Publication Type: Conference contribution, Conference Contribution

Publication year: 2019

Publisher: CEUR-WS

Pages Range: 42-53

Conference Proceedings Title: Proc. Conf. "Lernen, Wissen, Daten, Analysen"

Event location: Berlin, Germany DE

URI: http://ceur-ws.org/Vol-2454/paper_56.pdf

Open Access Link: http://ceur-ws.org/Vol-2454/paper_56.pdf


Data privacy is currently a topic in vogue for many organizations. Many of them run enterprise data lakes as data source for an ungoverned ecosystem, wherein they have no overview concerning data processing. They aim for mechanisms that require unsophisticated implementation, are easy to use, assume as little technical knowledge as possible, and enable their privacy officers to determine who processes which data when and for which purpose.

To overcome these challenges, we present a framework for query-driven enforcement of rule-based policies to achieve data-privacy compliance. Our framework can be integrated minimally intrusive in existing IT landscapes. In contrast to existing approaches, privacy officers do not require profound technical knowledge because our framework also enables non-experts in evaluating data processing in SQL queries by intuitionally comprehensive, tree-shaped visualizations. Queries can be classified as legal or illegal regarding data-privacy compliance. We provide a domain-specific language for defining policy rules that can be enforced automatically and in real-time.

Authors with CRIS profile

Related research project(s)

How to cite


Matschinske, J.O. (2019). Query-Driven Enforcement of Rule-Based Policies for Data-Privacy Compliance. In Robert Jäschke, Matthias Weidlich (Eds.), Proc. Conf. "Lernen, Wissen, Daten, Analysen" (pp. 42-53). Berlin, Germany, DE: CEUR-WS.


Matschinske, Julian O.. "Query-Driven Enforcement of Rule-Based Policies for Data-Privacy Compliance." Proceedings of the Lernen, Wissen, Daten, Analysen (LWDA) 2019, Berlin, Germany Ed. Robert Jäschke, Matthias Weidlich, CEUR-WS, 2019. 42-53.

BibTeX: Download