A Technique for Securing Big Data Using K-Anonymization With a Hybrid Optimization Algorithm

The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria Grey wolf-Cat Swarm Optimization (GWCSO) attaining in data. technique processed by adapting k- duplicating k records from original database. proposed GWCSO developed integrating Wolf Optimizer (GWO) Cat (CSO) constructing k-anonymized database, reveals only essential details end users hiding confidential information. experimental results of are compared with various existing performance metrics, such as Classification accuracy (CA) Information loss (IL). show that attains improved CA value 0.005 IL 0.798, respectively.