Enhancing Fine Grained Technique for Maintaining Data Privacy

Abstract

Privacy is a big challenge in a lot of fields such as healthcare field. As technology advances, data privacy became at risk and must to protect individuals' personal information and sensitive data. The existing privacy preserving techniques as encryption whether full or partial encryption are not sufficient because they have some defects. In this paper, we will introduce an enhanced technique whose main mission is maintaining individuals’ privacy and increasing the utility of data. By combining fine-grained encryption method with the proposed perturbation technique. Where we will use fine-grained encryption method to encrypt only key and sensitive attributes. On the other side, we will use proposed perturbation technique to add a small amount of random noise to quasi-identifier in order to increase the security of data and prevent disclosure it by linking with other external data. Our evaluation proves that the introduced technique is more secure than other privacy preserving techniques and consumes less memory.