A Novel Framework using Elliptic Curve Cryptography for Extremely Secure Transmission in Distributed Privacy Preserving Data Mining

TL;DR

This paper proposes a new framework that combines elliptic curve cryptography and data distortion techniques to ensure highly secure and privacy-preserving data mining across distributed databases.

Contribution

It introduces a novel framework integrating elliptic curve cryptography with data distortion for secure, privacy-preserving distributed data mining.

Findings

Enhanced security through elliptic curve cryptography

Effective privacy preservation with data distortion

Framework suitable for distributed data environments

Abstract

Privacy Preserving Data Mining is a method which ensures privacy of individual information during mining. Most important task involves retrieving information from multiple data bases which is distributed. The data once in the data warehouse can be used by mining algorithms to retrieve confidential information. The proposed framework has two major tasks, secure transmission and privacy of confidential information during mining. Secure transmission is handled by using elliptic curve cryptography and data distortion for privacy preservation ensuring highly secure environment.