Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms.

David M. Sommer; Lukas Abfalterer; Sheila Zingg; Esfandiar Mohammadi

Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms.

David M. Sommer, Lukas Abfalterer, Sheila Zingg, Esfandiar Mohammadi

Institut für IT-Sicherheit

Originalsprache	Englisch
Publikationsstatus	Veröffentlicht - 2021

UN SDGs

Dieser Output leistet einen Beitrag zu folgendem(n) Ziel(en) für nachhaltige Entwicklung

SDG 9 – Industrie, Innovation und Infrastruktur
SDG 11 – Nachhaltige Städte und Gemeinschaften
SDG 12 – Verantwortungsvoller Konsum und Produktion

Dokumente

https://dblp.org/rec/journals/corr/abs-2107-12957

Zitieren

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title = "Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms.",

author = "Sommer, \{David M.\} and Lukas Abfalterer and Sheila Zingg and Esfandiar Mohammadi",

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T1 - Learning Numeric Optimal Differentially Private Truncated Additive Mechanisms.

AU - Sommer, David M.

AU - Abfalterer, Lukas

AU - Zingg, Sheila

AU - Mohammadi, Esfandiar

N1 - DBLP's bibliographic metadata records provided through http://dblp.org/search/publ/api are distributed under a Creative Commons CC0 1.0 Universal Public Domain Dedication. Although the bibliographic metadata records are provided consistent with CC0 1.0 Dedication, the content described by the metadata records is not. Content may be subject to copyright, rights of privacy, rights of publicity and other restrictions.

PY - 2021

Y1 - 2021

M3 - Other

VL - abs/2107.12957

PB - arXiv

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