K-Anonymization approach for privacy preservation using data perturbation techniques in data mining

Abstract

Nowadays, privacy is a big issue for everyone because the internet generates and shares massive amounts of data every day. Because traditional privacy-preserving techniques do not protect sensitive data well enough, malicious attacks on sensitive data are possible. Traditional methods may have many drawbacks due to various attacks on sensitive data. Thus, data must be preserved before being shared with others. K-Anonymity protects privacy. By definition, anonymization means that a person is not identifiable, traceable, or reachable. Anonymization using k-anonymity protects data privacy and protects entire datasets. The traditional data perturbation method only works with numerical data, but the proposed method is designed to work with categorical data. The Proposed Framework perturbs data for multiple columns at once. The proposed framework's performance is evaluated in two ways. Initially, it keeps the data mining model accurate. Second, it protects the original data privacy while minimizing data loss. The main motivation for using k-anonymity is to remove or transfer personally identifiable information.