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dc.contributor.authorLei, Jingen_US
dc.contributor.authorCharest, Anne-Sophieen_US
dc.contributor.authorSlavkovic, Aleksandraen_US
dc.contributor.authorSmith, Adamen_US
dc.contributor.authorFienberg, Stephenen_US
dc.date.accessioned2019-09-20T11:52:40Z
dc.date.available2019-09-20T11:52:40Z
dc.date.issued2018-06
dc.identifier.citationJing Lei, Anne-Sophie Charest, Aleksandra Slavkovic, Adam Smith, Stephen Fienberg. 2018. "Differentially private model selection with penalized and constrained likelihood." Journal of the Royal Statistical Society: Series A (Statistics in Society), Volume 181, Issue 3, pp. 609 - 633. https://doi.org/10.1111/rssa.12324
dc.identifier.issn0964-1998
dc.identifier.urihttps://hdl.handle.net/2144/37968
dc.description.abstractSummary: In statistical disclosure control, the goal of data analysis is twofold: the information released must provide accurate and useful statistics about the underlying population of interest, while minimizing the potential for an individual record to be identified. In recent years, the notion of differential privacy has received much attention in theoretical computer science, machine learning and statistics. It provides a rigorous and strong notion of protection for individuals’ sensitive information. A fundamental question is how to incorporate differential privacy in traditional statistical inference procedures. We study model selection in multivariate linear regression under the constraint of differential privacy. We show that model selection procedures based on penalized least squares or likelihood can be made differentially private by a combination of regularization and randomization, and we propose two algorithms to do so. We show that our privacy procedures are consistent under essentially the same conditions as the corresponding non‐privacy procedures. We also find that, under differential privacy, the procedure becomes more sensitive to the tuning parameters. We illustrate and evaluate our method by using simulation studies and two real data examples.en_US
dc.format.extent609 - 633en_US
dc.languageen
dc.language.isoen_US
dc.publisherWileyen_US
dc.relation.ispartofJournal of the Royal Statistical Society: Series A (Statistics in Society)
dc.subjectStatistics & probabilityen_US
dc.subjectStatisticsen_US
dc.subjectEconometricsen_US
dc.titleDifferentially private model selection with penalized and constrained likelihooden_US
dc.typeArticleen_US
dc.description.versionAccepted manuscripten_US
dc.identifier.doi10.1111/rssa.12324
pubs.elements-sourcecrossrefen_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Arts & Sciencesen_US
pubs.organisational-groupBoston University, College of Arts & Sciences, Department of Computer Scienceen_US
pubs.publication-statusPublisheden_US
dc.date.online2017-10-10
dc.identifier.mycv379428
dc.identifier.mycv379428


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