ABSTRACT During past decades, the classroom scheduling problem has posed significant
challenges to educational programmers and teaching secretaries. In order to alleviate the
burden of the programmers, this paper presents Smart Class, which allows the programmers to
solve this problem using web services. By introducing service-oriented architecture (SOA),
Smart Class is able to provide classroom scheduling services with back-stage design space
exploration and greedy algorithms. Furthermore, the Smart Class architecture can be
dynamically coupled to different scheduling algorithms (e.g. Greedy, DSE, etc.) to fit in specific
demands. A typical case study demonstrates that Smart Class provides a new efficient
paradigm to the traditional classroom scheduling problem, which could achieve high flexibility by
software services reuse and ease the burden of educational programmers. Evaluation results on
efficiency, overheads and scheduling performance demonstrate the Smart Class has lower
scheduling overheads with higher efficiency.
ABSTRACT With banks reaching its users via mobile banking, it is becoming one of the essential feature that is demanded by almost every smartphone user. Mobile banking via a mobile browser is similar to internet banking. Browsing-based threats for smartphones are just the same as those for personal computers, elevating the need to focus on mobile security. Among the several authentication schemes, geolocation authentication is gaining importance as it is found most suitable for mobile devices. In this paper, GeoMoB, a dedicated secure mobile browser for mobile banking that makes use of multifactor authentication is designed and developed. GeoMoB features a geolocation based authentication scheme which ensures security of mobile transactions based on the user location.
With the popularity of cloud computing, mobile devices can store/retrieve personal data from anywhere at any time. Consequently, the data security problem in mobile cloud becomes more and more severe and prevents further development of mobile cloud. There are substantial studies that have been conducted to improve the cloud security. However, most of them are not applicable for mobile cloud since mobile devices only have limited computing resources and power. Solutions with low computational overhead are in great need for mobile cloud applications. In this paper, we propose a lightweight data sharing scheme (LDSS) for mobile cloud computing.
The convergence of mobile communications and cloud computing facilitates the cross-layer network design and content-assisted communication. Mobile video broadcasting can benefit from this trend by utilizing joint source-channel coding and strong information correlation in clouds. In this paper, a knowledge-enhanced mobile video broadcasting (KMV-Cast) is proposed.
In cloud service over crowd-sensing data, the data owner (DO) publishes the sensing data through the cloud server, so that the user can obtain the information of interest on demand. But the cloud service providers (CSP) are often untrustworthy. The privacy and security concerns emerge over the authenticity of the query answer and the leakage of the DO identity. To solve these issues, many researchers study the query answer authentication scheme for cloud service system. The traditional technique is providing DO's signature for the published data. But the signature would always reveal DO's identity. To deal with this disadvantage, this paper proposes a cooperative query answer authentication scheme, based on the ring signature, the Merkle hash tree (MHT) and the non-repudiable service protocol. Through the cooperation among the entities in cloud service system, the proposed scheme could not only verify the query answer, but also protect the DO's identity.
Clustering techniques have been widely adopted in many real world data analysis applications, such as customer behavior analysis, medical data Analysis, digital forensics, etc. With the explosion of data in today’s big data era, a major trend to handle a clustering over large-scale datasets is outsourcing it to HDFS platforms. This is because cloud computing offers not only reliable services with performance guarantees, but also savings on in-house IT infrastructures. However, as datasets used for clustering may contain sensitive information, e.g., patient health information, commercial data, and behavioral data, etc, directly outsourcing them to any Distributed servers inevitably raise privacy concerns.
The Cloud is increasingly being used to store and process big data for its tenants and classical security mechanisms using encryption are neither sufficiently efficient nor suited to the task of protecting big data in the Cloud. In this paper, we present an alternative approach which divides big data into sequenced parts and stores them among multiple Cloud storage service providers. Instead of protecting the big data itself, the proposed scheme protects the mapping of the various data elements to each provider using a trapdoor function.