Repeated Attribute Optimization for Big Data Encryption

Big data denotes the variety, velocity, and massive volume of data. Existing databases are unsuitable to store big owing its high volume. Cloud computing is an optimal solution process However, significant issue lies in handling access control privacy, wherein should be encrypted unauthorized user must restricted through efficient control. Attribute-based encryption (ABE) permits users encrypt decrypt for policy work practical scenarios, attributes repeated. In case specific policies, it not possible avoid attribute repetition even after application Boolean optimization approaches obtain a formula. For these there exists variety evaluated secret shares repeated attributes. Therefore, calculation cipher text irreducible policies seems lengthy computationally intensive. To address this problem, improved meta-heuristic-based on cipher-text policy-ABE (CP-ABE) developed study. Here, meta-heuristic concept phase, which returns optimized single share value each considering all shares. The only minimizes cost but also communication cost. Herein, sun flower (SFO), called newly updated SFO (NU-SFO) used perform CP-ABE. Finally, performance evaluation confirms reliability robustness scheme comparisons with traditional constructions.