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Map Reduce Top Down Approach For Scalability And Anonymization

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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.

Key Words

Anonymization; MRTDS; Suppression; Generalization

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

Publication Details

Published Paper ID: JETIR1707001
Registration ID: 170488
Published In: Volume 4 | Issue 7 | Year July-2017
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Page No: 1-4
ISSN Number: 2349-5162

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(Volume 5 | Issue 1 | January 2018 |Impact factor 5.87)

Call For Paper | Volume 5 | Issue 1 | Impact factor 5.87

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