Comparative Analysis of Privacy Preservation Mechanism: Assessing Trustworthy Cloud Services with a Hybrid Framework and Swarm Intelligence

Abstract

Cloud computing has emerged as a prominent field in modern computational technology, offering diverse services and resources. However, it has also raised pressing concerns regarding data privacy and the trustworthiness of cloud service providers. Previous works have grappled with these challenges, but many have fallen short in providing comprehensive solutions. In this context, this research proposes a novel framework designed to address the issues of maintaining data privacy and fostering trust in cloud computing services. The primary objective of this work is to develop a robust and integrated solution that safeguards sensitive data and enhances trust in cloud service providers. The proposed architecture encompasses a series of key components, including data collection and preprocessing with k-anonymity, trust generation using the Firefly Algorithm, Ant Colony Optimization for task scheduling and resource allocation, hybrid framework integration, and privacy-preserving computation. The scientific contribution of this work lies in the integration of multiple optimization techniques, such as the Firefly Algorithm and Ant Colony Optimization, to select reliable cloud service providers while considering trust factors and task/resource allocation. Furthermore, the proposed framework ensures data privacy through k-anonymity compliance, dynamic resource allocation, and privacy-preserving computation techniques such as differential privacy and homomorphic encryption. The outcomes of this research provide a comprehensive solution to the complex challenges of data privacy and trust in cloud computing services. By combining these techniques into a hybrid framework, this work contributes to the advancement of secure and effective cloud-based operations, offering a substantial step forward in addressing the critical issues faced by organizations and individuals in an increasingly interconnected digital landscape.