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A Privacy Preserving Anonymization Approach using Scalable Top Down Specialization and Randomization for Big Data Security

Athiramol S1

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-4 , Page no. 152-156, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i4.152156

Online published on Apr 30, 2019

Copyright © Athiramol S . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Athiramol S, “A Privacy Preserving Anonymization Approach using Scalable Top Down Specialization and Randomization for Big Data Security,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.152-156, 2019.

MLA Style Citation: Athiramol S "A Privacy Preserving Anonymization Approach using Scalable Top Down Specialization and Randomization for Big Data Security." International Journal of Computer Sciences and Engineering 7.4 (2019): 152-156.

APA Style Citation: Athiramol S, (2019). A Privacy Preserving Anonymization Approach using Scalable Top Down Specialization and Randomization for Big Data Security. International Journal of Computer Sciences and Engineering, 7(4), 152-156.

BibTex Style Citation:
@article{S_2019,
author = {Athiramol S},
title = {A Privacy Preserving Anonymization Approach using Scalable Top Down Specialization and Randomization for Big Data Security},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {7},
Issue = {4},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {152-156},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4010},
doi = {https://doi.org/10.26438/ijcse/v7i4.152156}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i4.152156}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4010
TI - A Privacy Preserving Anonymization Approach using Scalable Top Down Specialization and Randomization for Big Data Security
T2 - International Journal of Computer Sciences and Engineering
AU - Athiramol S
PY - 2019
DA - 2019/04/30
PB - IJCSE, Indore, INDIA
SP - 152-156
IS - 4
VL - 7
SN - 2347-2693
ER -

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Abstract

There are different analysis centers all around the world. These centers makes inferences that are beneficial to the society. From where did they get the data for doing such kind of analysis?. Is the data that is published for such kind of analysis is secure from privacy breach?. In America, different kind of records especially the medical records which contains disease information as a sensitive attribute are publishing publicly. This has to be taken as a serious issue. Taking into account these facts, different anonymization methods are developed. In this paper an approach that is different from all other approaches is proposed. The main concern is to manage background knowledge attack that most of the algorithms failed to heed. Although no algorithm is able to achieve 100 percent security without sacrificing information loss, a balance can be maintained between these two. This paper introduces an efficient method for handling the outlier tuples using a randomization algorithm.

Key-Words / Index Term

Background knowledge attack, IGPL metric , Quasi- identifier, K- anonymity, Randomization, Taxonomy tree, Top down specialization

References

[1] L. Xu, C. Jiang, J. Wang, J. Yuan, and Y. Ren, “Information security in big data: Privacy and data mining”, pp. 1149-1176, 2014.
[2] B.C.M. Fung, K.Wang, R. Chen and P. S. Yu, “Privacy preserving data publishing: A survey of recent developments”, ACM Computing surveys, pp. 1-53, 2010.
[3] Xuyun Zhang, Laurence T.Yang, Chang Liu, and Jinjun Chen, “A scalable top down specialization approach for data anonymization using Map Reduce on cloud”, IEEE Transactions on parallel and distributed systems, pp. 363-373, 2014.
[4] Benny Pinkas ,“Cryptographic techniques for privacy preserving data”, SIGKDD Explorations, pp. 12-19.
[5] L. Sweeney,“K-anonymity, A model for protecting privacy”,International Journal on Uncertainty, Fuzziness and Knowledge-based Systems, pp. 557-570, 2012.
[6] Ashwin Machanavajjhala, Johannes Gehrke, Daniel Kifer, “l- Diversity: Privacy beyond k- anonymity”, 2005.
[7] Ahmed Ali Mubark, Hatem Abdulkader, “Semantic anonymization in publishing categorical sensitive attributes”, IEEE International. Conference, pp. 89-95, 2016.
[8] B.C.M. Fung, K. Wang, and P. S. Yu, “Anonymizing classification data for privacy preservation”, IEEE Transactions Knowledge and Data Engineering, pp. 711-725, 2007.
[9] Savita Lohiya, LataRagha, “Privacy preserving in data mining using hybrid approach”, IEEE International Conference on Computational Intelligence and Communication Networks, pp. 743-746, 2012.