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A Data Security Framework for Cloud Computing Services

Luis-Eduardo Bautista-Villalpando1,*, Alain Abran2

1 Department of Electronic Systems, Autonomous University of Aguascalientes, Aguascalientes, 20131, Mexico
2 Department of Software Engineering and Information Technology, ETS, University of Quebec, Montreal, H3C 1K3, Canada

* Corresponding Author: Luis-Eduardo Bautista-Villalpando. Email: email

(This article belongs to the Special Issue: Cloud, SDN, and NFV architectures for next generation IoT infrastructures)

Computer Systems Science and Engineering 2021, 37(2), 203-218.


Cyberattacks are difficult to prevent because the targeted companies and organizations are often relying on new and fundamentally insecure cloud-based technologies, such as the Internet of Things. With increasing industry adoption and migration of traditional computing services to the cloud, one of the main challenges in cybersecurity is to provide mechanisms to secure these technologies. This work proposes a Data Security Framework for cloud computing services (CCS) that evaluates and improves CCS data security from a software engineering perspective by evaluating the levels of security within the cloud computing paradigm using engineering methods and techniques applied to CCS. This framework is developed by means of a methodology based on a heuristic theory that incorporates knowledge generated by existing works as well as the experience of their implementation. The paper presents the design details of the framework, which consists of three stages: identification of data security requirements, management of data security risks and evaluation of data security performance in CCS.


Cite This Article

L. Bautista-Villalpando and A. Abran, "A data security framework for cloud computing services," Computer Systems Science and Engineering, vol. 37, no.2, pp. 203–218, 2021.

cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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