Confidentiality Locking and Optimized Utilization with Data Deduplication in Cloud Storage

Data redundancy in cloud storage environments presents a significant challenge, necessitating effective deduplication strategies to optimize storage resources. Existing techniques often falter when confronted with the dynamic nature of cloud data, especially in handling encrypted data. This paper introduces a novel method implemented in Java using CloudSim (Cloud Simulator), which integrates Convergent Encryption (CE) and Modified Elliptic Curve Cryptography (MECC) algorithms. The primary objective of this approach is to develop secure data deduplication systems that simultaneously minimize data redundancy and ensure robust encryption in cloud storage environments. For users uploading new files, the proposed method utilizes the CloudSim simulation platform to encrypt files employing the Convergent Encryption (CE) technique. Subsequently, Modified Elliptic Curve Cryptography (MECC) is applied to add an extra layer of encryption, bolstering data security. The method efficiently identifies and reduces data redundancy at the block level, thereby contributing to enhanced storage optimization. Experimental results demonstrate the superiority of the proposed approach in terms of computational efficiency and security levels. This innovative solution, implemented in Java using CloudSim, not only addresses the dynamic nature of cloud data but also establishes a confidential and efficient framework for data storage in cloud environments. The utilization of CloudSim enhances the realism of the simulations, ensuring that the proposed method is not only theoretically effective but also practical in real-world cloud computing scenarios.