TY - GEN
T1 - A conceptual model of decision-making support for opening data
AU - Luthfi, Ahmad
AU - Janssen, Marijn
PY - 2017
Y1 - 2017
N2 - The trend of open data has spread widely in the government nowadays. The motivation to create transparency, accountability, stimulate citizen engagement and business innovation are drivers to open data. Nevertheless, governments are all too often reluctant to open their data as there might be risks like privacy violating and the opening of inaccurate data. The goal of the research presented in this paper is to develop a model for decision-making support for opening data by weighing potential risks and benefits using Bayesian belief networks. The outcomes can be used to mitigate the risks and still gain benefits of opening data by taking actions like the removing privacy-sensitive data from dataset. After the taking of actions the process can start over again and the risks and benefits can be weighed again. The iteration can continue till the resulting dataset can be opened. This research uses health patient stories dataset as an illustration of the iterative process. This shows how the decision-making support can help to open more data by decomposing datasets.
AB - The trend of open data has spread widely in the government nowadays. The motivation to create transparency, accountability, stimulate citizen engagement and business innovation are drivers to open data. Nevertheless, governments are all too often reluctant to open their data as there might be risks like privacy violating and the opening of inaccurate data. The goal of the research presented in this paper is to develop a model for decision-making support for opening data by weighing potential risks and benefits using Bayesian belief networks. The outcomes can be used to mitigate the risks and still gain benefits of opening data by taking actions like the removing privacy-sensitive data from dataset. After the taking of actions the process can start over again and the risks and benefits can be weighed again. The iteration can continue till the resulting dataset can be opened. This research uses health patient stories dataset as an illustration of the iterative process. This shows how the decision-making support can help to open more data by decomposing datasets.
KW - Bayesian belief networks
KW - Benefits
KW - Decision-making support
KW - Open data
KW - Risks
UR - http://www.scopus.com/inward/record.url?scp=85035151036&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-71117-1_7
DO - 10.1007/978-3-319-71117-1_7
M3 - Conference contribution
AN - SCOPUS:85035151036
SN - 9783319711164
VL - 792
T3 - Communications in Computer and Information Science
SP - 95
EP - 105
BT - E-Democracy – Privacy-Preserving, Secure, Intelligent E-Government Services - 7th International Conference, E-Democracy 2017, Proceedings
PB - Springer
T2 - 7th International Conference on eDemocracy, e-Democracy 2017
Y2 - 14 December 2017 through 15 December 2017
ER -