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

Igor Jakovljevic, Igor Jakovljevic, Christian Gütl, Andreas Wagner, Alexander Nussbaumer

2022

Abstract

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.

Paper Citation

in Harvard Style

Jakovljevic I., Gütl C., Wagner A. and Nussbaumer A. (2022). Compiling Open Datasets in Context of Large Organizations while Protecting User Privacy and Guaranteeing Plausible Deniability. In Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-583-8, pages 301-311. DOI: 10.5220/0011265700003269

in Bibtex Style

@conference{data22,
author={Igor Jakovljevic and Christian Gütl and Andreas Wagner and Alexander Nussbaumer},
title={Compiling Open Datasets in Context of Large Organizations while Protecting User Privacy and Guaranteeing Plausible Deniability},
booktitle={Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2022},
pages={301-311},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011265700003269},
isbn={978-989-758-583-8},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Compiling Open Datasets in Context of Large Organizations while Protecting User Privacy and Guaranteeing Plausible Deniability
SN - 978-989-758-583-8
AU - Jakovljevic I.
AU - Gütl C.
AU - Wagner A.
AU - Nussbaumer A.
PY - 2022
SP - 301
EP - 311
DO - 10.5220/0011265700003269