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IJSTR >> Volume 9 - Issue 1, January 2020 Edition



International Journal of Scientific & Technology Research  
International Journal of Scientific & Technology Research

Website: http://www.ijstr.org

ISSN 2277-8616



K-Anonymization Approach FOR Privacy Preserving IN Data Mining

[Full Text]

 

AUTHOR(S)

Vimalkumar B. Vaghela

 

KEYWORDS

Anonymization, Data Mining, Generalization, Information Loss, Information Santization, Privacy Preserving, Suppression

 

ABSTRACT

Data is collected and processed using diverse sources and tools that lead to privacy issues. Till now randomization, k-anonymization model, l diversity, t closeness, cryptography and many more techniques have been used to preserve the privacy of an individual. But each and every technique have their own demerits i.e. Information Loss, Privacy breached, Low Data Utility. Among all these approach, k anonymization approach one of the mostly used anonymization based approach. However, this approach suffers from the issue of information loss. So, it is challenging task for data miner to mine data. The paper focus to decrease information loss using the 2 level k anonymization approach and also preserve privacy as compared to the traditional approach. The main aim of this approach is to decrease data loss and no compromise with privacy.

 

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[20] Analysis of Various Sentiment Classification Techniques
[21] Sentiment Analysis using Support Vector Machine based on Feature Selection and Semantic Analysis
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