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Background: Big data is an emerging technology that has numerous applications in the fields, like hospitals, government records, social sites, and so on. As the cloud computing can transfer large amount of data through servers it has found its importance in big data. Hence, it is important in cloud computing to protect the data so that the third party users cannot access the information from the users. Methods: This paper develops an anonymization model and adaptive Dragon Particle Swarm Optimization (adaptive Dragon-PSO) algorithm for privacy preservation in cloud environment. The development of proposed adaptive DragonPSO incorporates the integration of adaptive idea in the dragon-PSO algorithm. The dragon-PSO is the integration of Dragonfly Algorithm (DA) and Particle Swarm Optimization (PSO) algorithm. The proposed method derives the fitness function for the proposed adaptive Dragon-PSO algorithm to attain the higher value of privacy and utility. The performance of the proposed method was evaluated using the metrics, such as information loss and classification accuracy for different anonymization constant values. Conclusion: The proposed method provided a minimal information loss and maximal classification accuracy of 0.0110 and 0.7415, respectively when compared with the existing methods.

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Nowadays, big data publishing is the emerging trend since they have good potential for the decision support in the applications, such as a hospital, government, industries, etc. Existing algorithms have many problems in preserving the privacy of the data when the data is in large size. To avoid these problems, this paper introduces a novel anonymity model for the data publishing based on K-DDD measure and MapReduce. This paper presents the Duplicate-Divergence-Different properties enabled dragon Genetic (DDDG) algorithm based on the k-DDD anonymization and the dragon operator based genetic algorithm. The proposed DDDG algorithm allows the privacy preservation in the big data by modifying the MapReduce techniques with the proposed DDDG algorithm. The performance of the proposed anonymity model is analyzed with the metrics such as information loss (IL) and the classification accuracy (CA). The adult database from the UC Irvine dataset is used for the simulation. The simulation results show that the proposed DDDG algorithm achieved the lowest IL of 0.0191 and the highest CA with the value of 0.8977 than the existing algorithms for k value of 2.