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

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedings – Annual report year: 2019Researchpeer-review

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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
Event3rd 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 201814 Sep 2018

Conference

Conference3rd 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
CountryIreland
CityDublin
Period10/09/201814/09/2018
SeriesLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11054 LNAI
ISSN0302-9743
CitationsWeb of Science® Times Cited: No match on DOI

    Research areas

  • Differential privacy, Rasch model, Student feedback
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