Preserving Privacy in Cloud Computing Environment Using Map Reduce Technique

Most of the cloud services require users to share personal data like electronic medical record for data analysis and data mining, bringing privacy concerns. The data sets can be anonymized by using generalization method to attain such privacy requirement. At present the proportion of data in several cloud application growing greatly in congruence with the bid data trend, therefore it is a challenge for currently used software tools to capture , maintain and process such large-scale data within a suffient time. Consequently , it is difficult for achieving privacy due to their inefficiency in handling large scale data sets. In this paper, we propose a scalable two phase top down specialization(TDS) approach to anonymize large-scale data sets using map reduce technique on cloud computing. To achieve the specialization computation in a highly scalable way, we draft a group of creative map reduce jobs in both the phases of our approach. As a result, the experimental evaluation shows that the scalability and efficiency of TDS can be significantly enriched over existing approaches.