Harnessing Data Science for Enhancing ERP Cloud Security and Data Integrity: A Review

Abstract

Cloud computing provides on-demand services over the internet, offering flexibility and cost savings. However, security and privacy risks hinder wider adoption. This review examines recent data science techniques to boost cloud security and data integrity. Specifically, trust models quantifying risks, task scheduling algorithms like Heterogeneous Earliest Finish Time (HEFT), and metaheuristic optimization methods like genetic algorithms are discussed. The strengths and weaknesses of these approaches are analyzed. Overall, a multi-pronged framework combining trust computation, heuristic scheduling, and metaheuristic optimization emerges as a robust paradigm for balancing security, efficiency, and quality of service in cloud environments. Additional research on computational trust, adaptive scheduling, and scalable optimization can help fully realize the potential of data science for advancing cloud security.