Efficient Privacy Preservation of Big Data Using Random Number Generators and Geometric Data Transformations
DOI:
https://doi.org/10.17762/msea.v71i3.164Abstract
Recent trends indicate that the volume of data stored in repositories is exploding due to the development of technology and the pervasive usage of web-based activities. This vast collection of data may contain personal information, which may occasionally pose privacy problems. The purpose of this research work is to establish privacy-preserving data publication mechanisms that are applicable to any numerical attributes and to design a distributed data model to process big data. Four distinct classifiers, namely Decision Tree, Naive Bayes, Adaboost, and KNN, are used to evaluate the classification accuracy of the suggested model. A geometric data perturbation-based method (RSUGP) and random number generators is used to protect sensitive data. For geometric data perturbation, a noise model based on random number generators was utilized instead of random noise or Gaussian noise. A Graphical neural network (GNN) is utilized for training, testing, and classification with an accuracy of 93%. Experiments indicate that the proposed strategy is superior to the other three in terms of attack resistance, classification precision, and runtime.