Results

eNauka >  Rezultati >  Differentially Private Synthetic Data with Private Density Estimation
Naziv: Differentially Private Synthetic Data with Private Density Estimation
Autori: Bojkovic, Nikolija; Loh, Po Ling
Godina: 2024
Publikacija: 2024 IEEE INTERNATIONAL SYMPOSIUM ON INFORMATION THEORY, ISIT 2024
ISSN: 2157-8095 Pretraži identifikator
Tip rezultata: Konferencijski rad
Kolacija: str. 599-604
DOI: 10.1109/ISIT57864.2024.10619641
WoS-ID: 001304426900102
Scopus-ID: 2-s2.0-85202870558
URI: https://enauka.gov.rs/handle/123456789/973056
Projekat: Cantab Capital Institute for the Mathematics of Information via the Philippa Fawcett Internship programme (Faculty of Mathematics, University of Cambridge)
Izvor metapodataka: (Preuzeto iz Nasi u WoS)
M-kategorija: 
Mp kategorija će biti prikazana naknadno.

Altmetric
Dimensions
Unpaywall

Google ScholarTM

Items in eNauka are protected by copyright, with all rights reserved, unless otherwise indicated.