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Abstract. Two most important goals of server assisted signature schemes are to aid small and mobile devices in computing digital signatures and to provide immediate revocation of signing capabilities. In this paper, we introduce an efficient scheme named server assisted one-time signature (SAOTS) alternative to server assisted signature scheme introduced by Asokan et al. Extended the Lamport's one-time signatures by utilizing hash chains, this new scheme's advantages are two-folds; first of all, it is communication-efficient running in fewer rounds, two instead of three, secondly, verification of server's signature can also be performed off-line resulting in real-time efficiency in computation as well as flexibility in the public-key signature scheme to be used. The experiments we have conducted showed that at least 40% gain in performance is obtained if SAOTS is preferred.

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Android mobile devices have reached a widespread use since the past decade, thus leading to an increase in the number and variety of applications on the market. However, from the perspective of information security, the user control of sensitive information has been shadowed by the fast development and rich variety of the applications. In the recent state of the art, users are subject to responding numerous requests for permission about using their private data to be able run an application. The awareness of the user about data protection and its relationship to permission requests is crucial for protecting the user against malicious software. Nevertheless, the slow adaptation of users to novel technologies suggests the need for developing automatic tools for detecting malicious software. In the present study, we analyze two major aspects of permission-based malware detection in Android applications: Feature selection methods and classification algorithms. Within the framework of the assumptions specified for the analysis and the data used for the analysis, our findings reveal a higher performance for the Random Forest and J48 decision tree classification algorithms for most of the selected feature selection methods.