Zobrazeno 1 - 10
of 377
pro vyhledávání: '"Gkoulalas-Divanis A"'
Autor:
Georgios Feretzakis, Konstantinos Papaspyridis, Aris Gkoulalas-Divanis, Vassilios S. Verykios
Publikováno v:
Information, Vol 15, Iss 11, p 697 (2024)
Generative AI, including large language models (LLMs), has transformed the paradigm of data generation and creative content, but this progress raises critical privacy concerns, especially when models are trained on sensitive data. This review provide
Externí odkaz:
https://doaj.org/article/63c7f6a37a194b8dae902042c62885ee
Autor:
Brown, J. Thomas, Yan, Chao, Xia, Weiyi, Yin, Zhijun, Wan, Zhiyu, Gkoulalas-Divanis, Aris, Kantarcioglu, Murat, Malin, Bradley A.
Supporting public health research and the public's situational awareness during a pandemic requires continuous dissemination of infectious disease surveillance data. Legislation, such as the Health Insurance Portability and Accountability Act of 1996
Externí odkaz:
http://arxiv.org/abs/2106.14649
Autor:
Choudhury, Olivia, Gkoulalas-Divanis, Aris, Salonidis, Theodoros, Sylla, Issa, Park, Yoonyoung, Hsu, Grace, Das, Amar
Federated learning enables training a global machine learning model from data distributed across multiple sites, without having to move the data. This is particularly relevant in healthcare applications, where data is rife with personal, highly-sensi
Externí odkaz:
http://arxiv.org/abs/2002.09096
Autor:
Rozita Tsoni, Evgenia Paxinou, Aris Gkoulalas-Divanis, Dimitrios Karapiperis, Dimitrios Kalles, Vassilios S. Verykios
Publikováno v:
Information, Vol 15, Iss 4, p 234 (2024)
Distance Learning has become the “new normal”, especially during the pandemic and due to the technological advances that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders bet
Externí odkaz:
https://doaj.org/article/e76b5aa215ae4bc793471c2ad833fa0f
Autor:
Choudhury, Olivia, Gkoulalas-Divanis, Aris, Salonidis, Theodoros, Sylla, Issa, Park, Yoonyoung, Hsu, Grace, Das, Amar
Leveraging real-world health data for machine learning tasks requires addressing many practical challenges, such as distributed data silos, privacy concerns with creating a centralized database from person-specific sensitive data, resource constraint
Externí odkaz:
http://arxiv.org/abs/1910.02578
Autor:
FERETZAKIS, Georgios, ANASTASIOU, Athanasios, PITOGLOU, Stavros, PAXINOU, Evgenia, GKOULALAS-DIVANIS, Aris, KALODANIS, Konstantinos, TSAPELAS, Ioannis, KALLES, Dimitris, VERYKIOS, Vassilios S.
Publikováno v:
Studies in Health Technology & Informatics; 2024, Vol. 321, p195-199, 5p
Publishing person-specific transactions in an anonymous form is increasingly required by organizations. Recent approaches ensure that potentially identifying information (e.g., a set of diagnosis codes) cannot be used to link published transactions t
Externí odkaz:
http://arxiv.org/abs/0912.2548
Autor:
FERETZAKIS, Georgios, KARAKOSTA, Christina, GKOULALAS-DIVANIS, Aris, KARAPIPERIS, Dimitris, GKONTZIS, Andreas F., PAXINOU, Evgenia, KOURENTIS, Christina, VERYKIOS, Vassilios S.
Publikováno v:
Studies in Health Technology & Informatics; 2024, Vol. 316, p863-867, 5p
Autor:
Tsoni, Rozita1 (AUTHOR) paxinou.evgenia@ac.eap.gr, Paxinou, Evgenia1 (AUTHOR) kalles@eap.gr, Gkoulalas-Divanis, Aris2 (AUTHOR) gkoulala@merative.com, Karapiperis, Dimitrios3 (AUTHOR) dkarapiperis@eap.gr, Kalles, Dimitrios1 (AUTHOR) verykios@eap.gr, Verykios, Vassilios S.1 (AUTHOR)
Publikováno v:
Information (2078-2489). Apr2024, Vol. 15 Issue 4, p234. 30p.
Publikováno v:
In Journal of Biomedical Informatics January 2017 65:76-96