An Evaluation of Projection Based Multiplicative Data Perturbation for KNN Classification

Random projections have recently emerged as a powerful method for dimensionality reduction. In random projection (RP), the original high-dimensional data is projected onto a lower-dimensional subspace using a random matrix whose columns have unit lengths. In this method the data is projected on to a random subspace, which preserves the approximate Euclidean distances between all pairs of points after the projection. In this research paper we give experimental results on using RP as a dimensionality reduction tool and analysis Projection Based Multiplicative data perturbation for KNN Classification as a tool for privacy-preserving data mining.