Abstract

This research paper addresses the enhancement of data security and privacy in cloud storage through diverse encryption methods, such as one-to-many encryption, data integrity, resilient data deletion, and privacy-preserving solutions. Leveraging technologies like Advanced Encryption Standards (AES), Rivest Shamir Adleman (RSA) and searchable encryption, the study integrates privacy-preserving techniques and machine learning in cloud environments, including the use of a load balancer with GitHub to optimize data distribution and storage management for a single user across multiple repositories. Additionally, the research delves into post-quantum encryption to fortify security measures against emerging threats, underscoring the ongoing need for exploring evolving data encryption technologies in cloud storage. The paper concludes by emphasizing the significance of continuous research aligned with identified security needs, with future plans to delve deeper into evolving security requirements and the role of load balancing in optimizing data storage and distribution.