A Robust Privacy Preserving Approach of Outsourced Data by Modified Frequent Web Access Pattern

Current scenario of large databases is in point of fact a major issue. Although, the conventional information examination seems to deal the extensive amounts of information. But the data analysts also attempt to analyze the productivity of data. This proposed work is an attempt to resolve the issue of digital information security by finding the highly frequent items in the dataset. Modified Frequent Web Access Pattern algorithm was developed in this work which find patterns in two scans. Technique called as super class substitution will be used here for perturbation of sensitive set of rules. It offers an added advantage of reducing the risk and the utility of database is also increased. Our experiment is carried out on a genuine dataset. The outcomes here, have shown that proposed work has better results over the previous methodologies.