The web documents are automatically interacting to discover the information by web mining, which is one of the applications of Cloud Computing (CC) technologies. These documents may be in the form of structured, semi-structured, or unstructured formats. In current web technologies, the Semantic Web is an extension for better enabling the people and computers to work together, where the information is well defined. Before storing the data to the cloud server, data owners should encrypt their data for privacy and security concerns. At the same time, the end-user, who is finding the data related to specific keywords, suggests the research on searchable encryption technique. In this research work, fine-grained authorization of search was achieved by developing the Attribute-Based Encryption (ABE) search technique, which is under the distribution of multiple attribute authorization. Finally, to validate this approach, an experimental study is conducted on Wikipedia as an ontology with existing techniques. This research applies the Attribute based encryption and search method for the effective search and improve security in the cloud. Access policy, cipher text and secret key is developed based on the Attribute selected from the data. The Lagrange interpolation method is applied for the search process and registration key is applied to access the data. The privacy preserving efficiency of the proposed model is 99.2 % and existing Hierarchical-ABE method has 96 % efficiency.

Presently, the distributed Resource Description Framework (RDF) partition the data across several computer nodes. In that, many existing RDF systems results in expensive query evaluation and high start-up cost. To address these issues, a new optimization algorithm: modified Grey Wolf Optimization (GWO) has been developed in this research paper. In conventional GWO algorithm, after finding the best values of , and , stopping criteria is accomplished. In modified GWO algorithm, after finding the best values of , and , the alpha value once again encircles the possible solutions for obtaining an optimal solution. In RDF data, query optimization is a challenging task, which has been effectively handled by modified GWO algorithm. In the experimental phase, modified GWO showed good performance in terms of execution time and memory usage as compared to the existing methodologies: Partial Evaluation and Centralized Assembly (PECA), Partial Evaluation and Distributed Assembly (PEDA), RDF-3X, Graph-Based SPARQL Query Engine (gStore), and Legato on Lehigh University Benchmark (LUBM) 10000 and DOREMUS 2017 datasets. Compared to these existing systems, the proposed system reduced the execution time around 2-5 minutes, and improves the precision, recall, and f-measure around 2-7%.

Medical data classification is used to find the hidden patterns of data by training a large amount of patient data collected from the providers. As the medical data is very sensitive, it must be a safeguard from all the noncollaborative means. Thus, it is important to take steps to preserve the confidential medical data. Accordingly, this paper proposes a classification method termed as crow optimization-based deep belief neural network (CS-DBN) to preserve the privacy of confidential medical data automatically. This classifier works based on three phases, including generation of the privacy-preserved data, construction of ontology, and classification. The Deep convolutional kernel approach is used to provide data confidentiality using the optimal coefficients. The construction of ontology is done with the cardiac heart disease terms used in the medical field for classification. Finally, the classification is performed using the deep belief network (DBN), which is trained using the crow search algorithm (CSA). The performance is analyzed in terms of the metrics, namely, accuracy, fitness, sensitivity, and specificity. The proposed CS-DBN method produces higher fitness, accuracy, sensitivity, and specificity of 0.9007, 0.8842, 1, and 0.8408, respectively.

Semantic web consists of the data in the structure manner and query searching methods can access these structured data to provide effective search result. The query recommendation in the semantic web relevance is needed to be improved based on the user input query. Many existing methods are used to improve the query recommendation efficiency using the optimization technique such as Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO). These methods involve in the use of many features which are selected from the user query. This in-turn increases the cost of a query in the semantic web. In this research, the query optimization was carried out by using the statistics method. The statistics based optimization method requires fewer features such as triple pattern and node priority etc., for finding the relevant results. The LUBM dataset contains the semantic queries and this dataset is used to measure the efficiency of the proposed Statistical based optimization method. The SPARQL queries are used to plot the query graph and triple scores are extracted from the graph. The cost value of the triple scores is measured and given as input to the proposed statistics method. The execution time of the statistics based optimization method for the query is 35 ms while the existing method has 48 ms.

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