The FireOak team keeps an eye out for and shares the most interesting articles, reports, and case studies related to managing, sharing, and securing information, data, and knowledge. Here are some snippets from what we’re reading right now. This week, our read examines a new model to analyse privacy violation risks of publishing open data.
With the push for open access and open data, it has become more commonplace to find institutions, governments, and organizations publishing their data. However, the exception to the rule is when datasets could potentially violate privacy regulations. Risking a potential privacy violation, in today’s climate, could bring about fines and reputational harm in addition to harming the the data subject or source. Exposing real user identities is a risk that weighs heavy on the decision to simply not publish datasets.
This paper, from the Proceedings of the 7th International Symposium on Business Modeling and Software Design 2017, proposes a privacy risk scoring model for open data architectures to analyze and reduce the risks associated with the opening of data. The scoring model utilizes a set of open data attributes reflecting privacy risks versus benefits.
Key Findings
- From the implemented cases, it was clear that different privacy risk mitigation measures are considered depending on risks associated with these attributes. Each defined privacy risk mitigation measure should be applied before making this dataset available online openly
- Further research is needed to define a common basis for the scoring matrix and all possible open data attributes.
- Statistical analysis should be conducted to validate the possible generalizability of the proposed model
- Details of the realization architecture should be discussed together with the implementation details of the privacy risk mitigation measures.
Citation:
Ali-Eldin, A., Zuiderwijk-van Eijk, A., & Janssen, M. (2017). Opening More Data: A New Privacy Risk Scoring Model for Open Data. In Proceedings of the 7th International Symposium on Business Modeling and Software Design 2017 (pp. 146-154) https://repository.tudelft.nl/islandora/object/uuid:0a86828c-97fd-48db-b33e-821711b36983
