Compiling Open Datasets in Context of Large Organizations while Protecting User Privacy and Guaranteeing Plausible Deniability

Open data and open science are terms that are becoming ever more popular. The information generated in large organizations is of great potential for organizations, future research, innovation, and more. Currently, there is a wide range of similar guidelines for publishing organizational data, focusing on data anonymization containing conflicting ideas and steps. These guidelines usually do not focus on the whole process of assessing risks, evaluating, and distributing data. In this paper, the relevant tasks from different open data frameworks have been identified, adapted, and synthesized into a six-step framework to transform organizational data into open data while offering privacy protection to organisational users. As part of the research, the framework was applied to a CERN dataset and expert interviews were conducted to evaluate the results and the framework. Drawbacks of the frameworks were identified and suggested as improvements for future work