Design of a provably secure biometrics-based multi-cloud-server authentication scheme
Abstract Big Data and Cloud of Things (CoT) are two inter-related research trends in our
data-driven society, and one research challenge is to design efficient security solution that
enables access to resources, services and data out-sourced to the cloud without
compromising the user's privacy. A viable solution is user authentication set-up for multi-
cloud-server designed to function as an expert system permitting its users to obtain the
desired services and resources (eg accessing data stored in a cloud storage account) from a …
data-driven society, and one research challenge is to design efficient security solution that
enables access to resources, services and data out-sourced to the cloud without
compromising the user's privacy. A viable solution is user authentication set-up for multi-
cloud-server designed to function as an expert system permitting its users to obtain the
desired services and resources (eg accessing data stored in a cloud storage account) from a …
Abstract
Big Data and Cloud of Things (CoT) are two inter-related research trends in our data-driven society, and one research challenge is to design efficient security solution that enables access to resources, services and data out-sourced to the cloud without compromising the user’s privacy. A viable solution is user authentication set-up for multi-cloud-server designed to function as an expert system permitting its users to obtain the desired services and resources (e.g. accessing data stored in a cloud storage account) from a cloud-server up on registration with a registration authority. Biometrics is a widely used authentication mechanism (e.g. in biometric passport); thus, in this paper, we devise a biometrics-based authentication scheme for multi-cloud-server environment deployment. To improve the accuracy of biometric pattern matching, we make use of bio-hashing. We then analyse the performance and efficiency of our scheme to demonstrate its utility.
Elsevier