Review of 'A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud'

Reviewer: Brijesh Mehta

Publication date of review: 2017-12-26

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Brijesh Mehta5

A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on CloudCrossref ScienceOpen

one more attempt towards scalable anonymization

Average rating:	    Rated 5 of 5.
Level of importance:	    Rated 5 of 5.
Level of validity:	    Rated 5 of 5.
Level of completeness:	    Rated 4 of 5.
Level of comprehensibility:	    Rated 5 of 5.
Competing interests:	None

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A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud

Xuyun Zhang, Laurence Yang, Chang Liu … (2014)

0 comments Cited 24 times     Rated -3 of 5. – based on 1 reviews

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Review information

DOI:: 10.14293/S2199-1006.1.SOR-UNCAT.AJKPEO.v1.RNKQYY

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This work has been published open access under Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Conditions, terms of use and publishing policy can be found at www.scienceopen.com.

Review text

The author proposed the scalable form of top-down specialization one the most popular anonymization technique. They have implemented TDS using Hadoop and claim that it provides same level of anonymization with low information loss. However, it is been observed that it requires all the data at the initial stage to generate taxonomy tree. It also suffers from data distribution problem of Hadoop.

Review of 'A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud'

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A Scalable Two-Phase Top-Down Specialization Approach for Data Anonymization Using MapReduce on Cloud

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