Differentially Private Medians and Interior Points for Non-Pathological Data

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dc.citation.journalTitleDROPS-IDN/v2/document/10.4230/LIPIcs.ITCS.2024.3en_US
dc.contributor.authorAliakbarpour, Maryamen_US
dc.contributor.authorSilver, Roseen_US
dc.contributor.authorSteinke, Thomasen_US
dc.contributor.authorUllman, Jonathanen_US
dc.date.accessioned2024-07-25T20:56:27Zen_US
dc.date.available2024-07-25T20:56:27Zen_US
dc.date.issued2024en_US
dc.description.abstractWe construct sample-efficient differentially private estimators for the approximate-median and interior-point problems, that can be applied to arbitrary input distributions over ℝ satisfying very mild statistical assumptions. Our results stand in contrast to the surprising negative result of Bun et al. (FOCS 2015), which showed that private estimators with finite sample complexity cannot produce interior points on arbitrary distributions.en_US
dc.identifier.citationAliakbarpour, M., Silver, R., Steinke, T., & Ullman, J. (2024). Differentially Private Medians and Interior Points for Non-Pathological Data. DROPS-IDN/v2/Document/10.4230/LIPIcs.ITCS.2024.3. 15th Innovations in Theoretical Computer Science Conference (ITCS 2024). https://doi.org/10.4230/LIPIcs.ITCS.2024.3en_US
dc.identifier.digitalLIPIcsITCS20243en_US
dc.identifier.doihttps://doi.org/10.4230/LIPIcs.ITCS.2024.3en_US
dc.identifier.urihttps://hdl.handle.net/1911/117535en_US
dc.language.isoengen_US
dc.publisherSchloss Dagstuhl - Leibniz Center for Informaticsen_US
dc.rightsExcept where otherwise noted, this work is licensed under a Creative Commons Attribution (CC BY) license.  Permission to reuse, publish, or reproduce the work beyond the terms of the license or beyond the bounds of fair use or other exemptions to copyright law must be obtained from the copyright holder.en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/en_US
dc.titleDifferentially Private Medians and Interior Points for Non-Pathological Dataen_US
dc.typeJournal articleen_US
dc.type.dcmiTexten_US
dc.type.publicationpublisher versionen_US
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