• Anju M Sunny
• Magniya Davis

Data is released in a published form for reuse by others, generally known as data publication or publishing. Up gradation of data to be a first class research output is the ultimate goal of this process. Sharing of delicate private data has become a cadre element of research. For privacy preserving and in order to afford increase of user data scalability a broad spectrum of techniques must be enforced, including data anonymization. K-anonymity, l-diversity, t-closeness etc. are the commonly used anonymization techniques for privacy preserving in data sets. In the existing system, generalization is the method used for k-anonymity. But it will not completely anonymize the sensitive data. In this paper, we put forward an integrated approach to anonymize large-scale data sets using the map-reduce framework. Top down Specialization (TDS) is done under the map-reduce framework. The data that is not anonymized is suppressed.

Anonymization; MRTDS; Suppression; Generalization

"Map Reduce Top Down Approach For Scalability And Anonymization ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.4, Issue 7, page no.1-4, July-2017, Available :http://www.jetir.org/papers/JETIR1707001.pdf

"Map Reduce Top Down Approach For Scalability And Anonymization ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.4, Issue 7, page no. pp1-4, July-2017, Available at : http://www.jetir.org/papers/JETIR1707001.pdf