In recent times, the utility and privacy are trade-off factors with the performance of one factor tends to sacrifice the other. Therefore, the dataset cannot be published without privacy. It is henceforth crucial to maintain an equilibrium between the utility and privacy of data. In this paper, a novel technique on trade-off between the utility and privacy is developed, where the former is developed with a metaheuristic algorithm and the latter is developed using a cryptographic model. The utility is carried out with the process of clustering, and the privacy model encrypts and decrypts the model. At first, the input datasets are clustered, and after clustering, the privacy of data is maintained. The simulation is conducted on the manufacturing datasets over various existing models. The results show that the proposed model shows improved clustering accuracy and data privacy than the existing models. The evaluation with the proposed model shows a trade-off privacy preservation and utility clustering in smart manufacturing datasets.

In real world as dependence on World Wide Web applications increasing day by day they transformed vulnerable to security attacks. Out of all the different attacks the SQL Injection Attacks are the most common. In this paper we propose SQL injection vulnerability prevention by decision tree classification technique. The proposed model make use famous decision tree classification model to prevent the SQL injection attacks. The proposed model will filter the sent HTTP request by using a decision tree classification based attack signatures. We test our proposed model on synthetic data which given satisfactory results.

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The MapReduce frame work is one which is proven that is as the best suitable framework which can be used to carry out Big data analytics. The big data analytics playing a vital role in real time data analysis applications. Where as in the conventional data mining techniques the clustering technique is proven as that the most useful technique for effective data analysis. From our literature review we found that there are no sufficient clustering techniques suitable for processing big data. Taking this as a disadvantage we are exploring the optimal grid clustering techniques for big data analysis using MapReduce architecture. The initial level experiments conducted using this proposed model is shown magnificent upshot.

Knowledge discovery, tasks are deals with huge no of records. Queries are needed to identify unique attributes values and their aggregate that is above a predefined threshold from this huge number of records. This type of queries is called iceberg queries. Iceberg queries requires huge amount of main memory and takes longer time to answer the query. As computer system has limited amount of main memory, the processing of iceberg queries is a challenging task. This paper discusses different methods that are in literature for processing iceberg queries , we also explore pros and cons of these methods and future scope.