<p class="Abstract">The amount of data processed and stored in the cloud is growing dramatically. The traditional storage devices at both hardware and software levels cannot meet the requirement of the cloud. This fact motivates the need for a platform which can handle this problem. Hadoop is a deployed platform proposed to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data of the cloud system. Hadoop has no strategy to assure the safety and confidentiality of the files saved inside the Hadoop distributed File system(HDFS). In the cloud, the protection of sensitive data is a critical issue in which data encryption schemes plays avital rule. This research proposes a hybrid system between two well-known asymmetric key cryptosystems (RSA, and Paillier) to encrypt the files stored in HDFS. Thus before saving data in HDFS, the proposed cryptosystem is utilized for encrypting the data. Each user of the cloud might upload files in two ways, non-safe or secure. The hybrid system shows higher computational complexity and less latency in comparison to the RSA cryptosystem alone.</p>
Developing a confident Hadoop essentially a cloud computing is an essential challenge as the cloud. The protection policy can be utilized during various cloud services such as Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS) and also can support most requirements in cloud computing. This event motivates the need of a policy which will control these challenges. Hadoop may be a used policy recommended to beat this big data problem which usually utilizes MapReduce design to arrange huge amounts of information of the cloud system. Hadoop has no policy to ensure the privacy and protection of the files saved within the Hadoop Distributed File System (HDFS). Within the cloud, the safety of sensitive data may be a significant problem within which encryption schemes play an avital rule. This paper proposes a hybrid method between pair well-known asymmetric key cryptosystems (RSA and Rabin) to cipher the files saved in HDFS. Therefore, before storing data in HDFS, the proposed cryptosystem is employed to cipher the information. In the proposed system, the user of the cloud might upload files in two ways, secure or non-secure. The hybrid method presents more powerful computational complexity and smaller latency as compared to the RSA cryptosystem alone.
The amount of data processed and stored in the cloud is growing dramatically. The traditional storage devices at both hardware and software levels cannot meet the requirement of the cloud. This fact motivates the need for a plat¬form which can handle this problem. Hadoop is a deployed platform proposed to overcome this big data problem which often uses MapReduce architecture to process vast amounts of data of the cloud system. Hadoop has no strategy to assure the safety and confidentiality of the files saved inside the Hadoop distributed File system (HDFS). In the cloud, the protection of sensitive data is a critical issue in which data encryption schemes plays avital rule. This research proposes a hybrid system between two well-known asymmetric key cryptosystems (RSA, and Paillier) to encrypt the files stored in HDFS. Thus before saving data in HDFS, the proposed cryptosystem is utilized for encrypting the data. Each user of the cloud might upload files in two ways, non-safe or secure. The hybrid system shows higher computational complexity and less latency in comparison to the RSA cryptosystem alone.
According to current study, individuals with cancer who have a gene mutation have a bad prognosis. Young women with breast cancer had a poorer prognosis than older women, although it is unknown if the p53 gene mutation contributed to this. Due in part to the devastation of cancer, the appropriate technology may help cancer sufferers in regaining their lives. Researchers seek for mutations in cancer-causing gene sequences in order to identify the precancerous stage. While genetic testing may be used to forecast some kinds of cancer, there is presently no effective technique for identifying all cancers caused by TP53 gene mutations. It is one of the most often discovered genetic anomalies in human cancer is a malfunction in the action of the protein P53. As a consequence, the Universal Mutation Database is used to identify gene mutations (UMDCell-line2010). The issue is that, although many basic databases (for example, Excel format databases) exist that include datasets of TP53 gene mutations associated with disease (cancer), this huge database is incapable of detecting cancer. Thus, the purpose the objective of this study is to create an approach for data mining that utilizes a neural network to ascertain the pre-cancerous state. To begin, bioinformatics techniques such as BLAST, CLUSTALW, and NCBI were used to determine whether or not there were any malignant mutations; second, the proposed method was carried out in two stages: to begin, bioinformatics techniques such as BLAST, CLUSTALW, and NCBI were used to determine whether or not there were any malignant mutations; and third, the proposed method was carried out in two stages: to begin, bioinformatics techniques such as To begin, bioinformatics tools such as BLAST and CLUSTAL Vote Algorithms were utilized to classify pre-cancer by malignant mutations in the disease's early stages. The writers teach and evaluate their subjects using a variety of situations, including cross validation and percentages. This page contains a review of the algorithms discussed before.
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