Data anonymization through generalization using map reduce on cloud

Abstract

Nowaday's cloud computing provides lot of computation power and storage capacity to the users can be share their private data. To providing the security to the users sensitive data is challenging and difficult one in a cloud environment. K-anonymity approach as far as used for providing privacy to users sensitive data, but cloud can be greatly increases in a big data manner. In the existing, top-town specialization approach to make the privacy of users sensitive data. 'When the scalability of users data increase means top-town specialization technique is difficult to preserve the sensitive data and provide security to users data. Here we propose the specialization approach through generalization to preserve the sensitive data and provide the security against scalability in an efficient way with the help of map-reduce. Our approach is founding better solution than existing approach in a scalable and efficient way to provide security to users data.

Enhancing Data Security in Cloud using Random Pattern Fragmentation and a Distributed NoSQL Database

“…Nevertheless the method proved challenging in the big data environment, apart from security issues it presented. Moreover, [15] proposed a top down specialization using MapReduce that consists of a more accuracy constraint MapReduce framework for data anonymization. Nevertheless, the proposed method had reduced extensibility and fault tolerance.…”

k-DDD Measure and MapReduce Based Anonymity Model for Secured Privacy-Preserving Big Data Publishing

“…The literature of the existing research works [1][2][3][4][5][6][7][8] related to privacy-preserving big data publishing using MapReduce is presented in this section.…”

“…Then, the position of the records in the cluster is changed by the dragon operator. After that, the fitness of each cluster is calculated by Equation (5). Finally, the optimization is performed for each step of the iteration t.…”

“…The enemy factor is given as, indicates the position of the enemy record in the cluster.The termination of the dragonfly operation is done at the final iteration. When the iteration M t = , the value of the ) the database which is changed due to the dragonfly operation.Step 6: Fitness calculationIn this step, the fitness of each cluster is calculated based on the Equation(5). The privacy and the utility value of each record in the cluster are calculated.…”