Schwab P, Langohr M, Meyer-Wegener K (2020)
Publication Language: English
Publication Type: Conference contribution, Conference Contribution
Publication year: 2020
Publisher: International Conference Proceeding Series (ICPS)
City/Town: New York, NY, United States
Pages Range: 30:1 - 30:4
Event location: Vienna, Virtual Conference
ISBN: 978-1-4503-8814-6
This paper explains the demonstration of the DataEconomist, a framework for policy-based SQL query classification according to data-privacy directives. Our framework automatically derives query meta-information based on query-log analysis and provides user-friendly, graphical interfaces for browsing and filtering queries based on this meta-information. We aim to complement existing data-privacy approaches and enable privacy officers to define domain-specific compliance policy rules based on the graphical filter mechanisms. Policies automatically classify queries as compliant or non-compliant regarding their processing of personal data.
During our demonstration, conference attendees assess our system in several scenarios. They filter queries based on various query meta-information, learn how to define compliance policies for automatic query classification without profound technical knowledge, and test this classification by formulating non-compliant queries.
APA:
Schwab, P., Langohr, M., & Meyer-Wegener, K. (2020). We Know What You Did Last Session: Policy-Based Query Classification for Data-Privacy Compliance With the DataEconomist. In Association for Computing Machinery (Eds.), Proceedings of the SSDBM 2020: 32nd International Conference on Scientific and Statistical Database Management (pp. 30:1 - 30:4). Vienna, Virtual Conference, AT: New York, NY, United States: International Conference Proceeding Series (ICPS).
MLA:
Schwab, Peter, Maximilian Langohr, and Klaus Meyer-Wegener. "We Know What You Did Last Session: Policy-Based Query Classification for Data-Privacy Compliance With the DataEconomist." Proceedings of the SSDBM 2020: 32nd International Conference on Scientific and Statistical Database Management, Vienna, Virtual Conference Ed. Association for Computing Machinery, New York, NY, United States: International Conference Proceeding Series (ICPS), 2020. 30:1 - 30:4.
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