IEEE 2012 HASBE: A Hierarchical Attribute-Based Solution for Flexible and Scalable Access Control in Cloud Computing

IEEE 2012 HASBE: A Hierarchical Attribute-Based Solution for Flexible and Scalable Access Control in Cloud Computing

IEEE 2012 TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY

Technology - Available in  J2EE 

Abstract— Cloud computing has emerged as one of the most influential paradigms in the IT industry in recent years. Since this new computing technology requires users to entrust their valuable data to cloud providers, there have been increasing security and privacy concerns on outsourced data. Several schemes employing attribute-based encryption (ABE) have been proposed for access control of outsourced data in cloud computing; however, most of them suffer from inflexibility in implementing complex access control policies. In order to realize scalable, flexible, and fine-grained access control of outsourced data in cloud computing, in this paper, we propose hierarchical attribute-set-based encryption (HASBE) by extending cipher text-policy attribute-set-based encryption (ASBE) with a hierarchical structure of users. The proposed scheme not only achieves scalability due to its hierarchical structure, but also inherits flexibility and fine-grained access control in supporting compound attributes of ASBE. In addition, HASBE employs multiple value assignments for access expiration time to deal with user revocation more efficiently than existing schemes. We formally prove the security of HASBE based on security of the cipher text-policy attribute-based encryption (CP-ABE) scheme by Bettencourt et al. and analyze its performance and computational complexity. We implement our scheme and show that it is both efficient and flexible in dealing with access control for outsourced data in cloud computing with comprehensive experiments.

IEEE 2012 : Separable Reversible Data Hiding in Encrypted Image

IEEE 2012 TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY

Technology - Available in DotNet / J2EE 

Abstract— This work proposes a novel scheme for separable reversible data hiding in encrypted images. In the first phase, a content owner encrypts the original uncompressed image using an encryption key. Then, a data-hider may compress the least significant bits of the encrypted image using a data-hiding key to create a sparse space to accommodate some additional data. With an encrypted image containing additional data, if a receiver has the data-hiding key, he can extract the additional data though he does not know the image content. If the receiver has the encryption key, he can decrypt the received data to obtain an image similar to the original one, but cannot extract the additional data. If the receiver has both the data-hiding key and the encryption key, he can extract the additional data and recover the original content without any error by exploiting the spatial correlation in natural image when the amount of additional data is not too large.

IEEE 2012: Offloading Android Applications to the Cloud without Customizing Android

IEEE 2012 PerCom Workshop on Pervasive Wireless Networking,

Technology - Available in Android & J2EE Web Server

Abstract— Spreading more than twice as fast as PCs, smart phones are quickly becoming the primary mean for Internet access. However, smart phones today are still constrained by limited computation resources such as CPU, memory and battery. In this paper, we present a framework that automatically offloads heavy back-end tasks of a regular standalone Android application to an Android virtual machine in the cloud. This framework can be deployed in the application layer without modifying the underlying Android platform. It also features three metrics that consider total response time, energy consumption and remaining battery life in deciding whether a task should be offloaded.

IEEE 2012: Prediction of User’s Web-Browsing Behavior: Application of Markov Model

IEEE TRANSACTIONS ON SYSTEMS AUGUST 2012

 Technology - Available in JAVA /J2EE 

Abstract— Web prediction is a classification problem in which we attempt to predict the next set of Web pages that a user may visit based on the knowledge of the previously visited pages. Predicting user’s behavior while serving the Internet can be applied effectively in various critical applications. Such application has traditional tradeoffs between modeling complexity and prediction accuracy. In this paper, we analyze and study Markov model and all-Kth Markov model in Web prediction. We propose a new modified Markov model to alleviate the issue of scalability in the number of paths. In addition, we present a new two-tier prediction framework that creates an example classifier EC, based on the training examples and the generated classifiers. We show that such framework can improve the prediction time without compromising Prediction accuracy. We have used standard benchmark data sets to analyze, compare, and demonstrate the effectiveness of our techniques using variations of Markov models and association rule mining. Our experiments show the effectiveness of our modified Markov model in reducing the number of paths without compromising accuracy. Additionally, the results support our analysis conclusions that accuracy improves with higher orders of all-Kth model