Opening More Data: A New Privacy Risk Scoring Model for Open Data

While the opening of data has become a common practice for both governments and companies, many datasets are still not published since they might violate privacy regulations. The risk on privacy violations is a factor that often blocks the publication of data and results in a reserved attitude of governments and companies. Additionally, even published data, which might seem privacy compliant, can violate user privacy due to the leakage of real user identities. This paper proposes a privacy risk scoring model for open data architectures to analyse and reduce the risks associated with the opening of data. The key elements consist of a new set of open data attributes reflecting privacy risks versus benefits trades-offs. Further, these attributes are evaluated using a decision engine and a scoring matrix intro a privacy risk indicator (PRI) and a privacy risk mitigation measure (PRMM). Privacy Risk Indicator (PRI) represents the predicted value of privacy risks associated with opening such data and privacy risk mitigation measures represent the measurements need to be applied on the data to avoid the expected privacy risks. The model is exemplified through five real use cases concerning open datasets.