A differential privacy workflow for inference of parameters in the rasch model

Teresa Anna Steiner, David Enslev Nyrnberg, Lars Kai Hansen*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

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 languageEnglish
Title of host publicationProceedings of ECML PKDD 2018 Workshops - MIDAS 2018 and PAP 2018
EditorsAnna Monreale, Carlos Alzate
PublisherSpringer
Publication date1 Jan 2019
Pages113-124
ISBN (Print)9783030134624
DOIs
Publication statusPublished - 1 Jan 2019
EventEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018 - Dublin, Ireland
Duration: 10 Sep 201814 Sep 2018
http://www.ecmlpkdd2018.org/

Conference

ConferenceEuropean Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2018
Country/TerritoryIreland
CityDublin
Period10/09/201814/09/2018
Internet address
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11054 LNAI
ISSN0302-9743

Keywords

  • Differential privacy
  • Rasch model
  • Student feedback

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