Secure and Efficient Approach for Enhancing Cloud Data Deduplication through Chaotic Elliptic Curve Cryptography

Abstract

The proposed research suggests a novel strategy for approaching the intersection of big data and cloud computing to scale obstacles encountered during the data deduplication procedure. As previously mentioned, data deduplication is a critical procedure that must be carried out when transferring information to and from the cloud. This will optimise the utilisation of network and storage resources. Unfortunately, the methods presently in use have deficiencies in terms of accuracy, dependability, and confidentiality, and encrypted data repetitions are frequently disregarded. Through the development of an improved algorithm for detecting and preventing data duplication in vast data sets, this study aims to address the identified shortcomings of its predecessor. By employing Chaotic Elliptic Curve Cryptography (ECC), the proposed methodology effectively fortifies the security and performance of data storage in the cloud. To mitigate the computational burden associated with keyword-based knowledge processing and interactive duplicate detection, the ECC protocol employs chaotic dynamics as opposed to conventional approaches. The research demonstrates potential in several domains, including secure identification of encrypted data based on composition, protection against malicious acts, and memory optimisation on cloud servers. The Chaotic ECC enhances the dependability and security of cloud-based data deduplication by reducing storage complexity and network overhead.