Elliptic Curve Diffie-Hellman based Privacy-Preserving Deduplication for Big Data in Cloud Systems

Data deduplication is crucial for cloud computing, particularly in managing the expansive growth of outsourced mobile data. Some existing approaches are vulnerable to brute-force attacks and single-point attacks, posing significant security risks. Furthermore, it causes substantial complexity on the resource-constrained edge nodes and the lack of support for cross-domain deduplication. To overcome this problem, this research aims to propose an effective and secure big data deduplication approach in cloud computing. This research proposes the novel combined encryption algorithm of the Elliptic Curve with Diffie Hellman (ECDH) for data privacy preservation and deduplication. The effectiveness and the security of the ECDH approach permit it to manage the enhanced data volumes in the multi-domain framework without significant performance degradation. The experimental results show that the proposed ECDH approach attains the effective communication overhead of data upload and data retrieval of 5.98Kb and 1.39Kb on the data size of 2Kb respectively as compared to the existing methods EC and DH.