Erection Trusted and Effective Request Services in the Cloud with RASP Data Perturbation

Cloud computing infrastructure made information accessible to public which has become an appealing solution for the advantages on scalability and cost-saving. However, some data is so sensitive that the data owner does not want to move to the cloud unless the data confidentiality and query privacy are guaranteed. The RASP data perturbation method provides secure and efficient range query and kNN query services for protected data in the cloud. The kNN-R algorithm works with the RASP range query algorithm to process the kNN queries. The nearest neighbors concept involves interpreting each entry in the database as a point in space. k Nearest Neighbors (kNN) algorithm selects k entries which are closest to the new point. However kNN algorithm performs slowly on large databases since each new entry has to be compared to every other entry. There is a alternative method proposed which is fit to the large sized databases. This method is FCNN i. e. fast condensed nearest neighbor data reduction method. In this method the database is summarized by finding only the important data points. The main purpose of this method is to approximate the nearest neighbor algorithm, 1NN, with a smaller, more representative set of data points.