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 |
|---|---|
| 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 | 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 - Dublin, Ireland Duration: 10 Sep 2018 → 14 Sep 2018 |
Conference
| Conference | 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 |
|---|---|
| Country | Ireland |
| City | Dublin |
| Period | 10/09/2018 → 14/09/2018 |
| Series | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11054 LNAI |
| ISSN | 0302-9743 |
Keywords
- Differential privacy
- Rasch model
- Student feedback
Cite this
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A differential privacy workflow for inference of parameters in the rasch model. / Steiner, Teresa Anna; Nyrnberg, David Enslev; Hansen, Lars Kai.
Proceedings of ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018. ed. / Anna Monreale; Carlos Alzate. Springer, 2019. p. 113-124 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 11054 LNAI).Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
TY - GEN
T1 - A differential privacy workflow for inference of parameters in the rasch model
AU - Steiner, Teresa Anna
AU - Nyrnberg, David Enslev
AU - Hansen, Lars Kai
PY - 2019/1/1
Y1 - 2019/1/1
N2 - 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.
AB - 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.
KW - Differential privacy
KW - Rasch model
KW - Student feedback
U2 - 10.1007/978-3-030-13463-1_9
DO - 10.1007/978-3-030-13463-1_9
M3 - Article in proceedings
SN - 9783030134624
SP - 113
EP - 124
BT - Proceedings of ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018
A2 - Monreale, Anna
A2 - Alzate, Carlos
PB - Springer
ER -