@inproceedings{47f80231d7e24ca6b0e6095cf75309b6,
title = "A differential privacy workflow for inference of parameters in the rasch model",
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.",
keywords = "Differential privacy, Rasch model, Student feedback",
author = "Steiner, {Teresa Anna} and Nyrnberg, {David Enslev} and Hansen, {Lars Kai}",
year = "2019",
month = jan,
day = "1",
doi = "10.1007/978-3-030-13463-1_9",
language = "English",
isbn = "9783030134624",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "113--124",
editor = "Anna Monreale and Carlos Alzate",
booktitle = "Proceedings of ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018",
note = "3rd Workshop on Mining Data for Financial Applications, MIDAS 2018 and 2nd International Workshop on Personal Analytics and Privacy, PAP 2018 held at 18th European Conference on Machine Learning and Knowledge Discovery in Databases, ECML PKDD 2018 ; Conference date: 10-09-2018 Through 14-09-2018",
}