Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

Fast-Sec: an approach to secure Big Data processing in the cloud

Anjos, JCSD, Galibus, T, Geyer, CFR, Fedak, G, Costa, JPCL, Pereira, R and de Freitas, EP (2017) Fast-Sec: an approach to secure Big Data processing in the cloud. International Journal of Parallel, Emergent and Distributed Systems. ISSN 1744-5760

Smart-Security.pdf - Accepted Version

Download (2MB) | Preview


Group Security is an important concern in computer systems, which is especially remarkable when the system has to handle large amounts of data and some different users accessing this data with different accessing permissions. This work proposes an innovative approach for providing a security infrastructure support to Big Data Analytic in Cloud-based systems named Fast-sec. Fast-Sec handles systems with large volumes of data from heterogeneous sources, in which users may access the system by different platforms, consuming or providing data. The security infrastructure proposed in Fast-Sec provides an authentication mechanism for users, and data access control adapted to high demands from cloud-based Big Data environment. The reported results show the adequacy of the proposed safety infrastructure to the cloud-based systems processing Big Data. © 2017 Informa UK Limited, trading as Taylor & Francis

Item Type: Article
Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in International Journal of Parallel, Emergent and Distributed Systems on 14th June 2017, available online: http://www.tandfonline.com/10.1080/17445760.2017.1334777
Uncontrolled Keywords: 0802 Computation Theory And Mathematics, 0805 Distributed Computing, 1702 Cognitive Science
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
Divisions: Computer Science & Mathematics
Publisher: Taylor & Francis
Date Deposited: 04 Oct 2017 10:38
Last Modified: 04 Sep 2021 03:47
DOI or ID number: 10.1080/17445760.2017.1334777
URI: https://researchonline.ljmu.ac.uk/id/eprint/7269
View Item View Item