Hybrid optimization‐based privacy preservation of database publishing in cloud environment

Abstract

This work intends to introduce an optimization-based privacy preservation model via selecting the optimal key matrix. Here, privacy preservation is carried out under two processes, namely, "data sanitization and restoration." In fact, data sanitization is the data preservation method, where the data (message) are preserved using the optimal key. Similarly, data restoration is the inverse procedure of sanitization. Here, the key matrix is optimally chosen using a novel hybrid algorithm. For optimization purpose, this work deploys a hybrid optimization approach known as random-based grey dragon algorithm (R-GDA) that involves the concepts of both "dragonfly algorithm (DA) and grey wolf optimization (GWO) algorithm." The novelty of the work is introduced in hybrid optimization approach R-GDA. Eventually, the supremacy of the adopted method is validated over other existing approaches in terms of various measures such as privacy, utility, and so on. The privacy preservation in the cloud is achievable in the field of education, banking sector, military, and the research community.

Restoring private autism dataset from sanitized database using an optimized key produced from enhanced combined PSO-GWO framework

“…The study of 17 discussed the procedures for yielding an optimal key that was employed in the sanitization process as well as the restoration process, but there were shortcomings for taking a longer time to update the key and no fixation of the number of key ranges. The same concern should be addressed for the study of 18 , 19 , where the outputs were lacking in optimization 18 and ineffectiveness to preserve other sensitive information, such as frequent items 19 . Another approach, namely the Bee-Foraging Learning-based Particle Swarm Optimization (BFL-PSO) algorithm, was introduced to yield the optimal key for data sanitization and restoration 20 .…”