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  • Microsimulations are used as a tool for evidence-based policy, to better understand the impact of policies on society. However, the quality of the outcome considerably depends on the quality of the data in use. In order to provide a rich set of variables on granular details, synthetic data may be involved. This becomes even more important in dynamic microsimulation where projection into the future is simulated or when providing an open data environment for the research community with a vast amount of variables including geocoded information. The present paper discusses opportunities and challenges of such synthetic but realistic data generation in a microsimulation data lab, which includes two steps of disclosure control. First, the base information which is used for the synthetic data generation has to be reviewed in terms of disclosure risks. Second, the data generating process must be ensured to not systematically reproduce rare events for (synthetic) individuals or replicating original input data. Otherwise, due to the large amount of additional information, this may lead to a cumulative effect of the individual re-identification risks. In order to make these output data of dynamic spatial microsimulations available in the sense of open and reproducible research, such statistical disclosure risks must be excluded a priori. This study examines whether disclosure risks may occur when synthetic data is generated via anonymized data from official statistics sources and how these can be avoided in principle. Furthermore, we discuss methods within the framework of spatial dynamic microsimulation frameworks that automatically ensure the standards of statistical disclosure control as well as official statistics data providers during simulation runs. (xsd:string)
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  • 2024 (xsd:gyear)
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  • 2024 (xsd:gyear)
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  • 10.1007/978-3-031-69651-0_21 ()
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  • 310 (xsd:string)
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  • english (xsd:string)
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  • 9783031696510 ()
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  • Privacy and Disclosure Risks in Spatial Dynamic Microsimulations (xsd:string)
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  • inproceedings (xsd:string)
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  • Privacy in Statistical Databases International Conference, PSD 2024, Antibes Juan-les-Pins, France, September 25–27, 2024, Proceedings (xsd:string)
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  • In Privacy in Statistical Databases International Conference, PSD 2024, Antibes Juan-les-Pins, France, September 25–27, 2024, Proceedings, edited by Domingo-Ferrer, Josep and Önen, Melek, 310-326, Springer Nature Switzerland, 2024 (xsd:string)
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  • 25.09.-27.09.2024 (xsd:gyear)
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  • Mikrozensus (MZ) (xsd:string)
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  • 2024 (xsd:string)
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  • english (xsd:string)
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  • 326 (xsd:string)
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