OCDP: An enhanced perturbation approach for data privacy protection

Abstract

With the exponential growth of internet and digital technology, there is a significant increase in the volume of personal data being collected, stored and shared across various platforms poses privacy risks including unauthorized access, misuse and exploitation. To mitigate these risks, effective privacy mechanisms are crucial. One such mechanism is Differential Privacy (DP) which aims to protect personal information by introducing noise into the data to obstruct individual identification. Though it effectively prevents breaches of personal information, a trade-off exists among privacy and accuracy. Additionally, DP often requires meticulous noise parameter tuning which can be complex and resource intensive. To overcome these challenges, this paper proposed the method named Opti-Cluster Differential Privacy (OCDP). The proposed OCDP is designed to automatically determine the optimal amount of noise for a dataset. The dataset is first divided into non-overlapping clusters using k-means clustering. It then employs a hybrid approach combining DP with Particle Swarm Optimization (PSO) to compute the optimal noise parameter (ε - epsilon) for each cluster. Based on this computed value, noise is added to each cluster and then it is merged to produce a final perturbed dataset. The Experimental results demonstrate that the proposed OCDP method achieves high privacy while being computationally efficient. The proposed OCDP method produces data with privacy percentages of 84 %, 88 %, 89 %, 85 %, 83 % and 77 % for the Heart Disease, GDM, Adult, Automobile, Thyroid Disease and Insurance datasets respectively representing 13 % (with clustering) and 50 % high (without clustering) when compared with other methods. Moreover, OCDP's computational efficiency allows for faster processing times making it reliable solution for maintaining privacy in large datasets.