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Summary With the proliferation of cloud storage services, data integrity verification becomes increasingly significant in order to guarantee the availability and correctness of the outsourced data. Recently, many auditing schemes have been proposed to verify data integrity without possessing or downloading the outsourced data files. However, such schemes in existence assume that there is a single data owner that can update the file and compute the integrity tags. They do not consider the efficiency of user revocation when auditing multi‐owner data in the cloud. In addition, existing schemes only support dynamic data operations over fixed‐size and single‐copy data blocks. Thus, every small update may need updating the tags for all file blocks in all replicas, which in turn causes higher storage and communication overheads. This paper proposes a public and dynamic auditing scheme that supports fully dynamic data operations over variable‐size data blocks for replicated and multi‐owner cloud storage. Moreover, the proposed scheme can also support efficient user revocation. By supporting batch auditing, the proposed scheme can handle multiple auditing tasks simultaneously and divide an auditing task into multiple sub‐tasks in order to increase the detection probability. Experimental results prove the effectiveness and efficiency of the proposed system. Copyright © 2016 John Wiley & Sons, Ltd.

SUMMARYUser authentication is a crucial requirement for cloud service providers to prove that the outsourced data and services are safe from imposters. Keystroke dynamics is a promising behavioral biometrics for strengthening user authentication, however, current keystroke based solutions designed for certain datasets, for example, a fixed length text typed on a traditional personal computer keyboard and their authentication performances were not acceptable for other input devices nor free length text. Moreover, they suffer from a high dimensional feature space that degrades the authentication accuracy and performance.In this paper, a keystroke dynamics based authentication system is proposed for cloud environments that is applicable to fixed and free text typed on traditional and touch screen keyboards. The proposed system utilizes different feature extraction methods, as a preprocessing step, to minimize the feature space dimensionality. Moreover, different fusion rules are evaluated to combine the different feature extraction methods so that a set of the most relevant features is chosen. Because of the huge number of users' samples, a clustering method is applied to the users' profile templates to reduce the verification time. The proposed system is applied to three different benchmark datasets using three different classifiers. Experimental results demonstrate the effectiveness and efficiency of the proposed system.