| Full text | |
| Author(s): Show less - |
Blanquer, Ignacio
[1]
;
Brasileiro, Francisco
[2]
;
Brito, Andrey
[2]
;
Calatrava, Amanda
[1]
;
Carvalho, Andre
[3]
;
Fetzer, Christof
[4]
;
Figueiredo, Flavio
[5]
;
Guimaraes, Ronny Petterson
[3]
;
Marinho, Leandro
[2]
;
Meira, Jr., Wagner
[5]
;
Silva, Altigran
[3]
;
Alberich-Bayarri, Angel
[6]
;
Camacho-Ramos, Eduardo
[6]
;
Jimenez-Pastor, Ana
[6]
;
Ribeiro, Antonio Luiz L.
[5]
;
Nascimento, Bruno Ramos
[5]
;
Silva, Fabio
[2]
Total Authors: 17
|
| Affiliation: | [1] Univ Politecn Valencia, Valencia - Spain
[2] Univ Fed Campina Grande, Campina Grande, Paraiba - Brazil
[3] Univ Fed Amazonas, Manaus, Amazonas - Brazil
[4] Tech Univ Dresden, Dresden - Germany
[5] Univ Fed Minas Gerais, Belo Horizonte, MG - Brazil
[6] QUIBIM, Valencia - Spain
Total Affiliations: 6
|
| Document type: | Journal article |
| Source: | FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE; v. 110, p. 119-134, SEP 2020. |
| Web of Science Citations: | 0 |
| Abstract | |
Medical data processing has found a new dimension with the extensive use of machine-learning techniques to classify and extract features. Machine learning strongly benefits from computing accelerators. However, such accelerators are not easily available at hospital premises, although they can be easily found on public cloud infrastructures or research centers. Nevertheless, the sensitivity of medical data poses several challenges on the access to such data, requiring security guarantees and isolation. In this paper we present an architecture that addresses this problem. It keeps critical data encrypted in memory and disk, which can only be accessed inside trusted execution environments protected by hardware extensions. Data is anonymized inside these environments and securely transferred to external sites that host accelerator devices, keeping the same network space and reducing security risks even in untrusted cloud backends. Results on the processing of data in different scenarios are presented and discussed. The results are demonstrated on a geographically-wide deployment provided by the ATMOSPHERE project. (C) 2020 Elsevier B.V. All rights reserved. (AU) | |
| FAPESP's process: | 15/24461-2 - A study of business models for the federation of services supporting e-Science |
| Grantee: | Francisco Vilar Brasileiro |
| Support Opportunities: | Research Projects - Thematic Grants |