1
Sri Krishna College of Engineering and Technology, Coimbatore, Tamil Nadu
2
Bannari Amman Institute of Technology, Sathyamangalam, Tamil Nadu
Abstract
Information is put away in various frameworks as emerging technologies allows influential data gathering and processing. Protection and security have turn into a longstanding challenging issue with advances in data and communication innovation. Privacy preserving data mining makes the customer information more secure by means of data perturbation and also it makes it harder to identify a person in an occurrence of data is spilled. Machine learning has got attention recently due to an energetic advancement of differential privacy (DP). DP is golden response scheme to address the privacy protection in analysis of data but it is quite hard to implement on real world data. The proposed system uses Bernstein polynomial function under differential privacy for perturbation. Heart disease dataset is used in this work to analyze the performance between the original and the modified dataset using DP with the classifier models decision tree, linear model, random forest, SVM, linear model and neural network. The experiment results show the minor variations in the accuracy, sensitivity and specificity measures.
N, K., & K, P. (2021). Differential Privacy Preservation Mechanism Using Bernstein Polynomial Function For Heart Disease Dataset. Int. J. of Aquatic Science, 12(3), 1662-1671.
MLA
Kousika N; Premalatha K. "Differential Privacy Preservation Mechanism Using Bernstein Polynomial Function For Heart Disease Dataset". Int. J. of Aquatic Science, 12, 3, 2021, 1662-1671.
HARVARD
N, K., K, P. (2021). 'Differential Privacy Preservation Mechanism Using Bernstein Polynomial Function For Heart Disease Dataset', Int. J. of Aquatic Science, 12(3), pp. 1662-1671.
VANCOUVER
N, K., K, P. Differential Privacy Preservation Mechanism Using Bernstein Polynomial Function For Heart Disease Dataset. Int. J. of Aquatic Science, 2021; 12(3): 1662-1671.