Data Anonymization Techniques for Preserving Privacy in Public Release Data Model: A Technical Review

Arun Amaithi Rajan¹ , Anitha Amaithi Rajan²

Section:Review Paper, Product Type: Journal-Paper
Vol.8 , Issue.1 , pp.58-62, Feb-2020

Online published on Feb 28, 2020

Copyright © Arun Amaithi Rajan, Anitha Amaithi Rajan . 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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Abstract :
The protection of sensitive records is very necessary for a modern scenario. Lately, the informational index is accessible for open use for statistical analysis. In this situation increasingly sensitive information like medical records, nation resident`s data, worker`s compensation data and so on are affecting to a higher extent since we are giving our data to people in general. Thus, Data anonymization assumes significance in the present day to protect the open discharge of sensitive information. In this paper, we reviewed some anonymization techniques and proposed a simple anonymization technique which is the combination of synthetic data generation and pseudonymization approach which reduces attacks on sensitive facts.

Key-Words / Index Term :
Anonymization techniques, privacy-preserving algorithm, synthetic data generation and pseudonymization technique

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