An improved privacy-preserving data mining technique using singular value decomposition with three-dimensional rotation data perturbation

Recent advancements in data mining have given rise to a new channel of research, coined as privacy-preserving data mining (PPDM). PPDM technology allows us to derive useful information from vast amounts of data while protecting privacy of individual records. This paper proposed a methodology based on the machine learning algorithm called singular value decomposition (SVD) and 3D rotation data perturbation (RDP) for preserving privacy of data. Decomposition and dimensionality reduction helps to eliminate sensitive information, and perturbed matrix is generated. The original and perturbed data are classified using different classifiers, and the performance is measured in terms of accuracy rate. Accuracy is the degree of correlation between the absolute observation and the actual observations. Experimental results revealed that the proposed scheme outperforms by achieving excellent accuracy for matrices of different sizes.