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The traditional Big Data model doesn't explicitly state for both structured and unstructured data sensitivity capture. In addition, the concept of privacy and security needs to be incorporated so that the risk of revealing personal data is subsequently mitigated. This paper proposes Secured Map Reduce Layer (SMR) in the big data structure for optimizing CPU utilization through Hybrid PSO/CS (Particle Swarm Optimization/Cuckoo Search) and Elliptic Curve Cryptographic approaches. This proposed method deals with choosing the private key for Authentication using either particle swarm optimization or cuckoo search algorithm and Provide and encryption and decryption through Key-Value Pair (KVP) of Elliptic Curve Cryptographic. HECC is implemented in a privacy layer called the Secured Map Reduce Layer (SMR). The proposed hybrid model combined with the Particle Swarm Optimization (PSO) that endorses the best value of global fitness for identifying the values and Cuckoo Search (CS) which generates the compiled post-process by finding with efficient authentication. This model provides a promise for privacy and protection in the data. It also solves privacy scalability problems such as CPU and memory optimization retains the trade-off of privacy-utility for big data. There is a remarkable improvement in running time and loss of information over current approaches and optimization of CPU and memory usage. Confidentiality is the process of the sender encrypting through a private key and the public key is utilized to decrypt the document. The protection of the entire algorithm suits a challenge compared with the related techniques through execution speed and accuracy.

Hybrid Elliptic Curve Cryptographic Approach for Data Privacy and Authentication in Secured Map Reduce Layer (SMR) for Optimized CPU Utilization