Privacy Preserving Data Mining by Using Implicit Function Theorem

TL;DR

This paper introduces a novel privacy-preserving data mining method using the implicit function theorem, transforming sensitive data through derivatives and eigenvalues for enhanced security.

Contribution

It proposes a new approach combining implicit function theorem with derivative and eigenvalue-based transformations for secure data mining.

Findings

Effective data perturbation through partial derivatives

Secure key generation using Jacobian eigenvalues

Implementation results demonstrate approach viability

Abstract

Data mining has made broad significant multidisciplinary field used in vast application domains and extracts knowledge by identifying structural relationship among the objects in large data bases. Privacy preserving data mining is a new area of data mining research for providing privacy of sensitive knowledge of information extracted from data mining system to be shared by the intended persons not to everyone to access. In this paper, we proposed a new approach of privacy preserving data mining by using implicit function theorem for secure transformation of sensitive data obtained from data mining system. we proposed two way enhanced security approach. First transforming original values of sensitive data into different partial derivatives of functional values for perturbation of data. secondly generating symmetric key value by Eigen values of jacobian matrix for secure computation. we given an example of academic sensitive data converting into vector valued functions to explain about our proposed concept and presented implementation based results of new proposed of approach.