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Volume IV Issue II

Author Name
D S DIBONG
Year Of Publication
2012
Volume and Issue
Volume 4 Issue 2
Abstract
Privacy is the ability of a person to control what information one reveals about oneself over the Internet. It consists of three things, “who can see information about you, when people can see information about you, and what information they can see about you. When end user interacts in web environment by surfing the Net, sending electronic mail messages and participating in online forums lot of data are generated and may be captured by third party tools and techniques; this may cause a breach in end user privacy. In web environment end user privacy is one of the most debatable legal issues. In this paper issues related to information leakage through inference techniques are presented. The role of available techniques and tools in privacy preservation is also discussed.
PaperID
2012/eusrm/04/02/1011

Author Name
E M Adigio, E O R Adetunji
Year Of Publication
2012
Volume and Issue
Volume 4 Issue 2
Abstract
The production environment for analytical data management applications is rapidly changing. Many enterprises are shifting away from deploying their analytical databases on high-end proprietary machines, and moving towards heaper, lower-end, commodity hardware, typically arranged in a shared-nothing MPP architecture, often in a virtualized environment inside public or private “clouds”. At t he same time, the amount of data that needs to be analyzed is exploding, requiring hundreds to thousands of machines to work in parallel to perform the analysis.A cloud OS is responsible for managing the cloud resources and its gives a high level interface to the application programmers in order to hide the infrastructure details. We describe Cloud MapReduce, an implementation of the MapReduce programming model on top of the Amazon cloud OS, which exploits the scalability offered by the cloud OS. Cloud MapReduce enjoys the inherit scalability and resiliency, which greatly simplies its architecture. Cloud MapReduce doesn’t need to design central coordinator components (like the NameNode and JobTracker in the Hadoop environment). They simply store the job progress status information in the distributed metadata store (SimpleDB). Cloud MapReduce doesn’t need to worry about scalability in the communication path and how data can be moved efficiently between nodes, all is taken care by the underlying CloudOS. Cloud MapReduce doesn’t need to worry about disk I/O issue because all storage is effectively remote and being taken care by the Cloud OS. First, it is faster than other implementations (e.g., 60 times faster than Hadoop in one case). Second, it is more scalable because it has no single point of bottleneck. Third, it is dramatically simpler with only 3,000 lines of co de (e.g., two orders of magnitude simpler than Hadoop). A cloud OS’ scalability comes at a price. To scale, the Amazon cloud OS not only relies on horizontal scaling, but it also adopts a weaker consistency model called eventual consistency. We describe how we overcome the limitations presented by horizontal scaling and the weaker consistency guarantee. We believe that building highly-scalable systems on top of a scalable cloud OS is a promising approach, and Cloud MapReduce is a concrete illustration.
PaperID
2012/eusrm/04/02/1025

Author Name
E O R Adetunji
Year Of Publication
2012
Volume and Issue
Volume 4 Issue 2
Abstract
Content-based image retrieval, a technique which uses visual contents to search images from large scale image databases according to users' interests, has been an active and fast advancing research area demands for image retrieval in multimedia field such as crime prevention, health informatics and biometrics has pushed application developers to search ways to manage and retrieve images more efficiently. .images was first annotated with text and then searched using a text-based approach from traditional database management systems. Content-based image retrieval uses the visual contents of an image such as color, shape, texture, and spatial layout to represent and index the image. In this paper, we show the different techniques of texture based retrieval. The texture is the property of image which shows the visible contents at certain scale. Texture in an image is a calculated matrices of pattern designing which gives us information about the rearrangement of intensities in a selected region of an image. We also construct texture from small region of image.
PaperID
2012/eusrm/04/02/1031

Author Name
A Akala, Nzekwe N M
Year Of Publication
2012
Volume and Issue
Volume 4 Issue 2
Abstract
- A mobile ad hoc network (MANET) is a collection of mobile nodes that temporarily integrate with each other to form a network. Such a network does not require the existence of a typical network infrastructure. There is no central entity with the authority to administer the services and configurations of the network. How to secure a MANET is an active field of study for researchers. However, most of the research on the topic of securing the MANETs has focused on adapting security mechanisms that were meant for traditional wired networks. This adaptation has resulted in security solutions that do not work efficiently or that make assumptions that are not in line with the properties and characterizations of MANETs. In this paper, we propose the use of security mechanisms for MANETs that are designed based on the characteristics, functionalities, and goals of such networks. We aim to initiate a paradigm shift in securing MANETs, in which the focus should be on building security solutions specifically developed for MANETs, and not on adapting solutions that were meant for conventional wired networks. We revisit the basics and propose a simple encryption keys creation scheme that is based on the Diffie-Hellman key agreement protocol. The work presented in this paper should mark the initiation of a research agenda designed to build security primitives that are specifically for MANETs, along the lines of the new paradigm.
PaperID
2012/eusrm/04/02/1034