Big-data driven approaches in materials science on cloud storage API through optimal homomorphic encryption

Big Data is a massive collection of data that require secure information transmission in the network. Hence, various existing Cryptography techniques have been used to improve information security in big data. However, the existing techniques cause data extrusion due to truncated transformation and diffusion properties in encryption and key generation processes that cause data leakage and loss. Hence, a novel Enhanced Encryption with Quantum-based Neuro Cryptography technique is introduced in which encryption is performed using logical shift and modulo operation based transformation that removes data extrusion and due to stream of photons in Quantum Cryptography Key has been generated and converted to the secret key via neuro cryptography. This process enhanced diffusion property and removed leakage and data loss. Moreover, malicious activities are happening due to inflexible encryption processes thus reducing confusion property that leads to delay and attack on nonresistance. Hence, a novel Inherited process with cyclic crossover operation has been developed in which one-point cyclic crossover and bit string mutation are achieved to combine ciphertext and key that increase confusion property and eliminate timing attack. The proposed system increases throughput and reduces encryption and decryption time with high diffusion and confusion properties.

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Big-data driven approaches in materials science on cloud storage API through optimal homomorphic encryption

Cited by 2 publications

References 19 publications

Cryptanalysis and security evaluation of optimized algorithms for image encryption in deep optimal network

Cryptanalysis and security evaluation of optimized algorithms for image encryption in deep optimal network

Enhanced cryptography techniques using inherited process with cyclic crossover operation in big data

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