Book chapter · Conference paper
A differential privacy workflow for inference of parameters in the rasch model
Department of Applied Mathematics and Computer Science, Technical University of Denmark1
Algorithms and Logic, Department of Applied Mathematics and Computer Science, Technical University of Denmark2
Technical University of Denmark3
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark4
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.
| Language: | English |
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| Publisher: | Springer |
| Year: | 2019 |
| Pages: | 113-124 |
| Proceedings: | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018 |
| Series: | Lecture Notes in Computer Science (including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| Journal subtitle: | Midas 2018 and Pap 2018, Dublin, Ireland, September 10-14, 2018, Proceedings |
| ISBN: | 3030134628 , 3030134636 , 9783030134624 and 9783030134631 |
| ISSN: | 03029743 |
| Types: | Book chapter and Conference paper |
| DOI: | 10.1007/978-3-030-13463-1_9 |
| ORCIDs: | 0000-0001-8541-0284 , 0000-0003-2434-2534 , 0000-0002-8583-9408 , 0000-0002-4112-067X , 0000-0002-2827-7613 , 0000-0001-5145-3438 , Steiner, Teresa Anna and Hansen, Lars Kai |
