Secure and Privacy-Preserving Data Migration Techniques in Cloud Ecosystems

Abstract

With the rapid rise in multi-cloud and hybrid cloud environments, data migration has become one of the most requested and sought after efficient and secure ways of data transfer. Nonetheless, data migration between different types of cloud environments is fraught with serious security and privacy issues, such as unauthorized access or data breaches and non-compliance with various regulations. The paper will discuss the state-of-the-art methods of safe and privacypreserving data migration in the cloud ecosystem. We provide an in-depth review of the existing approaches, including encryption-based models, privacy-preserving cryptographic protocols, and secure transfer frameworks. Besides that, we also develop a layered migration framework that incorporates homomorphic encryption, access control policies as well as data anonymization to protect sensitive data both in transit and at rest. The framework is compared to the main security criteria, such as confidentiality, integrity, and privacy preservation. It demonstrates the minimization of attack surfaces and a higher adherence rate to data protection regulations. Since cloud migration processes raise the problem of data
sovereignty, trust, and regulatory compliance, our results indicate the need to adopt proactive security architectures to support these processes. The research is relevant to the creation of secure cloud migration patterns according to the dynamic cybersecurity environment.