Abstract
The Rasch model is used to estimate student performance and task difficulty in simple test scenarios. We design a workflow for enhancing student feedback by release of difficulty parameters in the Rasch model with privacy protection using differential privacy. We provide a first proof of differential privacy in Rasch models and derive the minimum noise level in objective perturbation to guarantee a given privacy budget. We test the workflow in simulations and in two real data sets.
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CITATION STYLE
Steiner, T. A., Nyrnberg, D. E., & Hansen, L. K. (2019). A differential privacy workflow for inference of parameters in the rasch model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11054 LNAI, pp. 113–124). Springer Verlag. https://doi.org/10.1007/978-3-030-13463-1_9
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