For securing embedded system, deprived of vital resources, the foremost task is to ensure data integrity and security of the data stored in untrusted memory. As on chip storage is limited in secure processor, designer is forced to push data and program code on an untrusted external memory which is always fully susceptible and open to adversarial tampering. This calls for efficient process that verifies and ensures integrity of data stored in untrusted zone. Available memory integrity technique like Merkle trees suffers from a disadvantage of a very high communication overhead as processor has to quiz memory too often to perform integrity check. This slows down processor capability. Proposed technique suggests two algorithms and performs integrity check with low communication overhead and less silicon area occupancy.
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Main problem with high dimensional datasets is that not all the measured entities are “significant” for gaining knowledge about the data during data mining process. High dimensional datasets have many mathematical challenges but there are many opportunities for the same. While many computationally expensive methods can construct predictive models with high accuracy for such as data but it is still of interest for many applications to reduce the dimension of the original data prior to modeling of the data. This work is about Dimensionality Reduction, which in turn is about converting data of very high dimensionality into data of much lower dimensionality such that each of the lower dimensions conveys much more information.
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In this research work a new clustering technique is introduced to predict the behaviour of user based on time clustering. Web Usage Mining is described as the finding along with study of user access patterns, through the web log files and related data from the Web site. Web usage mining is basically concerned with finding out what users are looking for on the internet (With the help of various data sources). A large number of websites are coming into existence daily and number of internet users are growing daily at a very fast rate. There are millions of internet users from different geographical areas. The users are of different types, they are of different age groups, educational level and living in different geographical locations. Furthermore the way and motive of accessing the web is different. So to take care of all these different kind of users, the Websites companies are interested in enquiring about the users who are accessing the websites. So there should be a way to differentiate between the different users, so that their requirements can be taken care of. The distinguishing of users can be done by refining the data gathered from the various sources (Weblog etc.), and the clustering the data by applying different clustering technique.So that we can get more accurate result and then these results can be applied to Websites for their popularity and improvement in user friendliness.
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The level of increase in the severity of diseases and the level of insecurity at travel are alarming to the greater need of organ transplantations. It is therefore very essential to have complete details of organ donors, aiming to donate their organs; after death, by making their organs immortal and thus giving life to the needy. Clustering is the one of the best methodologies in data mining for classifying and categorising data. Maintaining details and tracking complete medical backgrounds of pre-enrolled organ donors is no way a simple task, noise level in such types especially at outlier’s matters a lot. Thus K-medoids is generally used, which minimizes the sum of dissimilarities between each object and its corresponding reference point. This paper refers the process of aiming at a improvising the most traditional algorithm, k-means as it is very likely to fail at identifying outliers which in turn effect the clusters formed; by k-medoid which chooses data points as centres termed as exemplars or medoid and generally works with an arbitrary matrix of distances between data points to extract correct information about the donors.
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Several security schemes for data sharing on un trusted servers have been proposed. In these approaches, data owners store the encrypted data files in un trusted storage and distribute the corresponding decryption keys only to authorized users. Thus, unauthorized users as well as storage servers cannot learn the content of the data files because they have no knowledge of the decryption keys. However, the complexities of user participation and revocation in these schemes are linearly increasing with the number of data owners and the number of revoked users, respectively. By setting a group with a single attribute, Lu et al. Proposed a secure provenance scheme based on the cipher text policy attribute-based encryption technique, which allows any member in a group to share data with others. In this paper, we propose a novel Mona protocol for secure data sharing in cloud computing. Our proposed scheme is able to support dynamic groups efficiently. Specifically, new granted users can directly decrypt data files uploaded before their participation without contacting with data owners.
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Wireless mesh network is a communication network made up of radio nodes organized in a mesh topology. Wireless mesh-networks is a new technology in wireless communication. WMN’s an emerging technology, may bring the dream of a seamlessly connected world into reality. WMN’s easily, effectively and wirelessly connect entire cities using inexpensive, existing technology. AODV routing protocol also used in WMN’s. AODV use simple request-reply mechanism for the discovery of routes. In this paper study on wireless mesh network and AODV routing protocol . It cover introduction & working of WMN’s, advantages, types, applications and characteristics.
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The number of smart phone users to use micro security Digital card (Micro SD card) for the propose of storing multimedia files are growing rapidly, but the smart phone users facing challenge of limited energy capacity/ performance (e.g., battery life, storage), environment (e.g., scalability, and availability), and security (e.g., reliability and privacy) of these devices has not been solved satisfactorily in smart phone users. The area of MCC (Multimedia Cloud Computing) the limitation on energy capacity, environment and security capacity can be erased off in an efficient way by offloading heavy tasks to the cloud. That means we evaluate this problems (Performance, environment and security) in multimedia applications on smart phones that are connected to Multimedia Cloud Computing (MCC).I have conducted an extensive set of experiments to measure the energy costs to investigate whether or not smart phones save energy by using MCC services. In other words, I investigate the feasibility of MCC to provide the offloading technique and OSGi (Open Service Gateway Initiative)- based mobile cloud service model. Specifically, I compared the energy costs for uploading and downloading a video file to and from MCC with the energy costs of encoding the same video file on a smart phone. The aforementioned comparison was performed by using HTTP and FTP Internet protocols with 3G and Wi-Fi network interfaces.
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Cloud computing is a computing platform that inhabits in a service provider’s large data centre and is able to energetically offer servers the ability to address a wide range of needs of clients. The cloud is a symbol for the internet. Some people call it the World Wide Computer. Technically, it is a computing archetype in which tasks are assigned to a combination of connections, software and services accessed over a network.
This paper provides brief details about the cloud computing with an overview of key features to give a sight about the novel focused technology.
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The contending revelries who hold secretive data may collaboratively conduct privacy preserving distributed data analysis (PPDA) tasks to studyadvantageous data models or analysis results. The competing parties have different spurs.
Although certain PPDA techniques guarantee that nothing other than the final analysis result is revealed, it is impossible to verify whether or not participating parties are truthful about their private input data. Unless accuratespurs (incentives) are set, even current PPDA techniques cannot prevent participating parties from modifying their private inputs. The ability to communicate and share data has many benefits, and the idea of an omniscient data source carries great value to research and building accurate data analysis models.
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