A privacy preservation model for big data in map-reduced framework based on k-anonymisation and swarm-based algorithms. (20th February 2020)

• Record Type: Journal Article
• Authors: Madan, Suman; Goswami, Puneet
• Is Part Of: International journal of intelligent engineering informatics. Volume 8:Number 1(2020)
• Page Start: 38
• Page End: 53
• Publication Date: 2020-02-20
• Subjects: big data publishing -- cloud computing -- k-anonymisation -- privacy -- swarm-based algorithm -- PSO -- particle swarm optimisation -- dragonfly -- fitness function -- MapReduce; Artificial intelligence -- Engineering applications -- Periodicals; Engineering -- Computer programs -- Periodicals; Knowledge management -- Periodicals; 620.0028563
• Languages: English
• ISSNs: 1758-8715

Abstract

In recent years, two mainstream technologies have become the centre of IT world, big data and cloud computing. Both these fields are fundamentally different but used together generally. The big-data deals with huge scales of data however cloud-computing is majorly about the infrastructure. Together these fields are beneficial for enterprises varying from the government sector to social sites, from academic to medical sectors, etc. Thus, it becomes important to safeguard the datasets so that the end-users of data may not access the information delivered by the users. This paper presents a hybrid k-anonymisation model for the map-reduce framework which guarantees the preservation of privacy in the cloud database using the combination of swarm-based algorithms. The proposed model focuses on deriving fitness function which will give high value of privacy and low information loss. The simulation and comparison with other algorithms shows better privacy and utility when working with proposed model.