Design and Development of Privacy Preservation Approach in Data Mining Using Multivariate Framework in Continuous and Multi-Dimensional Data
Shailesh Kumar Vyas, Swapnili P. Karmore · 2024
Data mining raises serious privacy issues. For the purpose of protecting privacy in data mining, techniques have been developed. Privacy-preserving data mining for multi-dimensional data sets, however, results in significant data loss, information leakage. An innovative method called Multidimensional Noise Additive Model (MNAM), which improves cluster identification and privacy while reducing information leakage, is suggested in this study. Anonymization technique that incorporates aggregation is applied in multi-dimensional data sets which leads to the reduction of information leakage and enhances the privacy. The domain-specific range of the subspaces are taken into consideration when performing noise addition. The MNAM method considers Euclidian distances among the neighbouring clusters. Then, anonymized subspaces are subjected to random noise inside the subspace domain to improve cluster detection and minimize data loss.