Optimized Damage Assessment in Large Datasets in Cloud

Given the many advantages of cloud computing, many organizations, including those managing critical information systems, have been opting to move their data and applications to clouds. However, storing a large volume of time sensitive critical data in clouds brings about major security challenges. If a cyberattack on the cloud system succeeds in affecting the critical data, the damage spreads through the database rapidly due to the interdependency nature of such data. Without a fast and efficient damage assessment and recovery process, many critical applications will be impacted resulting in the unavailability of the vital operations of such systems. In this paper, we present a model that can accelerate damage assessment, and therefore recovery, of a large and interdependent data set by quickly separating affected and unaffected zones and releasing the unaffected data to be used by the corresponding applications when the recovery of the affected data continues.