Main Article Content
As the usage of internet and web applications emerges faster, security and privacy of the data is the most challenging issue which we are facing, leading to the possibility of being easily damaged. Various conventional techniques are used for privacy preservation like condensation, randomization and tree structure etc., the limitations of the existing approaches are, they are not able to maintain proper balance between the data utility and privacy and it may have the problem with privacy violations. This paper presents an Additive Rotation Perturbation approach for Privacy Preserving Data Mining (PPDM). In this proposed work, various dataset from UCI Machine Learning Repository was collected and it is protected with a New Additive Rotational Perturbation Technique under Privacy Preserving Data Mining. Experimental result shows that the proposed algorithm’s strength is high for all the datasets and it is estimated using the DoV (Difference of Variance) method.