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.
Original language | English |
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Title of host publication | Proceedings of ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018 |
Editors | Anna Monreale, Carlos Alzate |
Publisher | Springer |
Publication date | 1 Jan 2019 |
Pages | 113-124 |
ISBN (Print) | 9783030134624 |
DOIs | |
Publication status | Published - 1 Jan 2019 |
Event | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018 - Dublin, Ireland Duration: 10 Sept 2018 → 14 Sept 2018 http://www.ecmlpkdd2018.org/ |
Conference
Conference | European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018 |
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Country/Territory | Ireland |
City | Dublin |
Period | 10/09/2018 → 14/09/2018 |
Internet address |
Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 11054 LNAI |
ISSN | 0302-9743 |
Keywords
- Differential privacy
- Rasch model
- Student feedback