Data Mining with Privacy Protection Using Precise Elliptical Curve Cryptography

Abstract

Protecting the privacy of data in the multi-cloud is a crucial task. Data mining is a technique that protects the privacy of individual data while mining those data. The most significant task entails obtaining data from numerous remote databases. Mining algorithms can obtain sensitive information once the data is in the data warehouse. Many traditional algorithms/techniques promise to provide safe data transfer, storing, and retrieving over the cloud platform. These strategies are primarily concerned with protecting the privacy of user data. This study aims to present data mining with privacy protection (DMPP) using precise elliptic curve cryptography (PECC), which builds upon that algebraic elliptic curve in finite fields. This approach enables safe data exchange by utilizing a reliable data consolidation approach entirely reliant on rewritable data concealing techniques. Also, it outperforms data mining in terms of solid privacy procedures while maintaining the quality of the data. Average approximation error, computational cost, anonymizing time, and data loss are considered performance measures. The suggested approach is practical and applicable in real-world situations according to the experimental findings.