A GA-based virtual machine migration technique to optimize data privacy and integrity
Shiladitya Bhattacharjee, Sulabh Bansal, Tanupriya Choudhury · 2025
Migrations of applications or full virtual machines (VMs) are crucial for meeting customer demand at a satisfactory level for any cloud service provider. Security of data, particularly privacy and integrity, is thus an additional significant consideration throughout the migration procedure. Several unique security issues might arise during migration, including transmission errors, interference from unauthorized parties, channel limitations, bandwidth issues, and others. The transfer of big VMs makes these difficulties even worse. However, it is extremely difficult to preserve both data integrity and privacy throughout the migration process. Frequently the endeavor to safeguard data privacy leads to an augmentation in data size, potentially resulting in data loss and impeding data integrity or vice versa. Numerous scholars have attempted to address these problems holistically, but no satisfactory answer has yet been found in the literature. Therefore, we have created a hybrid system that incorporates a lightweight encryption method based on genetic algorithms (GAs) to safeguard privacy, and a unique error control mechanism to reduce transmission error, and data loss, and to maintain adequate data integrity. The results of the experiment demonstrate its ability to provide enhanced privacy by delivering greater Avalanche Effect and Entropy values. High-Throughput value, low percentage of Information Loss, and high signal to noise ratio value prove the effectiveness of the study in preserving data integrity.