Zobrazeno 1 - 10
of 88
pro vyhledávání: '"ÜNSAL, Ayşe"'
Autor:
Ünsal, Ayşe, Önen, Melek
This work inspects a privacy metric based on Chernoff information, \textit{Chernoff differential privacy}, due to its significance in characterization of the optimal classifier's performance. Adversarial classification, as any other classification pr
Externí odkaz:
http://arxiv.org/abs/2403.10307
In this paper, we present a stealthy and effective attack that exposes privacy vulnerabilities in Graph Neural Networks (GNNs) by inferring private links within graph-structured data. Focusing on the inductive setting where new nodes join the graph a
Externí odkaz:
http://arxiv.org/abs/2307.13548
Autor:
Ünsal, Ayşe, Önen, Melek
This paper studies the statistical characterization of detecting an adversary who wants to harm some computation such as machine learning models or aggregation by altering the output of a differentially private mechanism in addition to discovering so
Externí odkaz:
http://arxiv.org/abs/2105.05610
Autor:
Ünsal, Ayşe1 kelebek.0681@hotmail.com, Ayyıldız, Tülay Kuzlu1
Publikováno v:
Journal of the Child / Çocuk Dergisi. Jun2024, Vol. 24 Issue 2, p92-98. 7p.
Autor:
Ünsal, Ayşe1 kelebek.0681@hotmail.com, Ayyıldız, Tülay Kuzlu1
Publikováno v:
Journal of the Child / Çocuk Dergisi. Mar2024, Vol. 24 Issue 1, p36-42. 7p.
Autor:
ÜNSAL, AYŞE1 ayse.unsal@eurecom.fr, ÖNEN, MELEK1 melek.onen@eurecom.fr
Publikováno v:
ACM Computing Surveys. Mar2024, Vol. 56 Issue 3, p1-18. 18p.
The work identifies the fundamental limits of coded caching when the K receiving users share {\Lambda}$\leq$ K helper-caches, each assisting an arbitrary number of different users. The main result is the derivation of the exact optimal worst-case del
Externí odkaz:
http://arxiv.org/abs/1809.09422
Publikováno v:
In Transfusion and Apheresis Science December 2022 61(6)
Akademický článek
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Autor:
Ünsal, Ayşe, Knopp, Raymond
This work considers distributed sensing and transmission of sporadic random samples. Lower bounds are derived for the reconstruction error of a single normally or uniformly-distributed finite-dimensional vector imperfectly measured by a network of se
Externí odkaz:
http://arxiv.org/abs/1309.0448