A Random Projection Approach to Strengthen the Privacy Level of Medical Images
A. Viji Amutha Mary · Journal of Computational and Theoretical Nanoscience · 2019
All our day’s work in this world of science is done with useful data. These useful data or information is extracted from raw facts. From the information, knowledge is gained. This knowledge is used by the customers for beneficial outcome. Here comes the concept of data mining. An important query arises as how to preserve these data. This concept is called as Privacy Preserving with Data Mining (PPDM). Many PPDM techniques are available to protect the data. The PPDM technique is useful in fields like medicine, forensics, defence, etc to preserve the confidential data. The existing techniques protect the secret data either by perturbing or by hiding them. Moreover, most of the techniques focus only on the numerical data. Very few perturbation techniques like translation, multiplicative and rotation perturb the images. But these techniques are very easily attacked by third parties since the transformation is a linear one. The Research Work aims to overcome the drawbacks of the existing perturbation techniques. The main objective of the Research Work is to improve the privacy level of the images by implementing a Random Projection (RP) technique. The remarkable features and benefits of the RP technique are pinpointed.