K-Anonymization-Based Temporal Attack Risk Detection Using Machine Learning Paradigms

Abstract

Huge amount of personal data is collected by online applications and its protection based on privacy has brought a lot of major challenging issues. Hence, the [Formula: see text]-anonymization with privacy-preserving data publishing has emerged as an active research field. The published data contains personalized information, which may be used for analysis converting it to useful information. In this paper, Quasi identifier (QI) data publishing with data preservation through the [Formula: see text]-anonymization process is proposed. Moreover, the risks such as the temporal attack in the previous release of re-identifying QI information are evaluated using the [Formula: see text]-anonymity model. The development of independent and ensemble classifiers for finding efficient QI’s to avoid temporal attacks is the major objective of this paper. Therefore, the classifiers like Naïve Bayes, Support Vector Machine, and Multilayer Perceptron are used as base classifiers. An ensemble model based on these base classifiers is also used. The experimental results demonstrate that, the proposed classification approach is an effective K-anonymity tool for the enhancement of sequential release.