Abstract
The recent techniques built on cloud computing for data processing is scalable and secure, which increasingly attracts the infrastructure to support big data applications. This paper proposes an effective anonymization based privacy preservation model using k-anonymization criteria and Grey wolf-Cat Swarm Optimization (GWCSO) for attaining privacy preservation in big data. The anonymization technique is processed by adapting k- anonymization criteria for duplicating k records from the original database. The proposed GWCSO is developed by integrating Grey Wolf Optimizer (GWO) and Cat Swarm Optimization (CSO) for constructing the k-anonymized database, which reveals only the essential details to the end users by hiding the confidential information. The experimental results of the proposed technique are compared with various existing techniques based on the performance metrics, such as Classification accuracy (CA) and Information loss (IL). The experimental results show that the proposed technique attains an improved CA value of 0.005 and IL value of 0.798, respectively.
Highlights
The advancements in big data led to several opportunities for research in the upcoming years
This paper proposes an effective anonymization-based privacy-preservation model using k-anonymization criteria and grey wolf-cat swarm optimization (GWCSO) for attaining privacy preservation in big data
This paper proposes a privacy preservation model using k-anonymization criteria and GWCSO for achieving secure communication in the cloud platform while transmitting the data to the end user
Summary
The advancements in big data led to several opportunities for research in the upcoming years. The Big data is adapted for discovering knowledge using different sectors of society. The big data has spanned the way for making the decisions in a right way. Due to the sharing of data, several concerns related to security are generated. As the big data handles the data of a large number of users, the privacy is an important task, which needs to be accomplished for protecting the data (Yang, et al, 2014), (Youke, et al, 2020). The privacy and security is a major challenge in big data. The big data is not accepted if privacy and security are not addressed. Atiewi et al, 2020) is another major issue when the conventional preservation technique is adapted in big data.
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