The complexity involved in migrating data between systems in a cloud computing environment is much higher than that in local networks. This complexity creates unprecedented research opportunities for people working in this area. This paper deals with data migration between systems in a cloud environment. The authors address the domain-specific needs associated with this problem. Specifically, they propose an algorithm, SCON-UP.

SCON-UP uses a clustering-based method for the configuration of interfaces. The algorithm addresses user preferences in terms of dealing with information privacy by supporting different aspects of information security. These aspects correspond to various scenarios of data usage. This negates the idea of having one security measure to be used for all situations. For example, the security measures required for personal health are much more essential compared to downloading music from the cloud. This also leads to the idea of distributed security that is able to adapt to the security needs of the data for the required situation.

The authors very nicely describe the above-mentioned challenges using a pilot study that involved three different types of scenarios: email, credit card transactions, and web browsing history. In this pilot study, the users were asked to provide their level of security interactions. The results of the study were analyzed using ANOVA for email and credit card transactions, and the t-test for web browsing.

Overall, this very well-written paper describes the current state-of-the-art and related work, and offers a case study in a scholarly manner. The philosophy and research findings described will be very useful for future research studies in this field.