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