Differentially Private Medians and Interior Points for Non-Pathological Data

Simple item page
dc.citation.journalTitleDROPS-IDN/v2/document/10.4230/LIPIcs.ITCS.2024.3
dc.contributor.authorAliakbarpour, Maryam
dc.contributor.authorSilver, Rose
dc.contributor.authorSteinke, Thomas
dc.contributor.authorUllman, Jonathan
dc.date.accessioned2024-07-25T20:56:27Z
dc.date.available2024-07-25T20:56:27Z
dc.date.issued2024
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.
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.2024.3
dc.identifier.digitalLIPIcsITCS20243
dc.identifier.doihttps://doi.org/10.4230/LIPIcs.ITCS.2024.3
dc.identifier.urihttps://hdl.handle.net/1911/117535
dc.language.isoeng
dc.publisherSchloss Dagstuhl - Leibniz Center for Informatics
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.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleDifferentially Private Medians and Interior Points for Non-Pathological Data
dc.typeJournal article
dc.type.dcmiText
dc.type.publicationpublisher version
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
LIPIcsITCS20243.pdf
Size:
841.64 KB
Format:
Adobe Portable Document Format
Download
Collections
Faculty Publications
Computer Science Publications