The improved DROP security based on hard AI problem in cloud. (2016) - Sanjeevi, P.; Balamurugan, G.; Viswanathan, P.
Authorising data to administrative control through third-party, is employed in the cloud, gives growth to security concerns. The data mollification may occur due to attacks by other users and nodes within the cloud. Therefore, high safety measures are essential to protect data within the cloud. In this paper, we present a new security primitive based on hard AI problems explicitly novel relations of a graphical secret key system built on top of Captcha technology, which we call Captcha as graphical passwords (Carp). CaRP also narrative an approach to address the familiar image hotspot problem in popular graphical password systems, such as PassPoints, that often leads to weak password choices. The DROP methodology fragments the file and replicates the fragmented data over the cloud nodes. Each of the nodes stores only a single fragment of a particular data that pledges that even if there should be an attack no substantial data is uncovered to the attacker. Furthermore, the nodes storing the fragments are divided with certain expanse by means of graph T-colouring to prohibit an attacker of predicting the locations of the fragments. So, this paper extends DROP methodology with AES algorithm to protect the outsourced data in the cloud and integrate them with CaRP to improve the online security by 14.4%.