• P. Shiva MCA., Nadar Mahjana Sangam S.Vellaichamy Nadar College.
  • Dr. P. Karthiga M.Phil., Ph.D., Nadar Mahjana Sangam S.Vellaichamy Nadar College
  • Dr. X. J. J. Anitha M.Phil., Ph.D., Nadar Mahjana Sangam S.Vellaichamy Nadar College


Cloud, data anonymization, data partition, privacy preservation, Top Down Specifications (TDS)


The large number of cloud services such as like Application,Platform,Infrastructure (IaaS,PaaS,SaaS)requires the users want to share the private data like for data analysis or data mining, data anonymization, bringing privacy concerns privacy may be data sets via generalization to satisfy certain security and privacy requirements such as k-anonymity and store them categorized of preserving techniques. At present nowadays the data cloud applications are increasing their large-scale data within the Big data trend huge amount of data sets and preserving sensitive, large scale data is very difficult data sets due to their map reduce is design by two phase of this technique to archieve scalable two phase Top Down Specifications (TDS) is scalability and efficient (TDS) is significance improved over existing approaches. TheMap reduce approach is a framework and this widely adopted for parallel data processing to address the scalability problem of the top-down specialization(TDS) approach for large scale data Anonymization. TDS approach is widely used for data Anonymization that provides a good arbitrate between data utility and data consistency. Most of the TDS algorithm is centralized, that are insufficient to handle large-scale data sets. we introduce a highly scalable two phase TDS approach for data Anonymization by using the map reduce framework on cloud.


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How to Cite

P. Shiva, Dr. P. Karthiga, & Dr. X. J. J. Anitha. (2016). A SYSTEM TO ANALYSIS REAL TIME BIGDATA USING TOPDOWN SPECIALIZATION. International Education and Research Journal (IERJ), 2(6). Retrieved from https://ierj.in/journal/index.php/ierj/article/view/322