An evaluation on big data generalization using k-Anonymity algorithm on cloud

Abstract

Nowadays data security plays a major issue in cloud computing and it remains a problem in data publishing. Lot of people share the data over cloud for business requirements which can be used for data analysis brings privacy as a big concern. In order to protect privacy in data publishing the anonymization technique is enforced on data. In this technique the data can be either generalized or suppressed using various algorithms. Top Down Specialization (TDS) in k-Anonymity is the majorly used generalization algorithm for data anonymization. In cloud the privacy is given through this algorithm for data publishing but another bigger problem is scalability of data. When data is tremendously increased on cloud which is shared for the data analysis there anonymization process becomes tedious. Big Data helps here in a way that large scale data can be partitioned using mapreduce framework on cloud. In our approach the data is anonymized using two phases Map phase and Reduce phase using Two Phase Top Down Specialization (Two Phase TDS) algorithm and the scalability and efficiency of Two Phase TDS is experimentally evaluated.