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IJSTR >> Volume 9 - Issue 4, April 2020 Edition



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

Website: http://www.ijstr.org

ISSN 2277-8616



Enhanced L-Diversity Algorithm And Improved Particle Swarm Optimization (IPSO) Algorithm For Data Leakage Detection

[Full Text]

 

AUTHOR(S)

Arul Selvi A, Joe Prathap P M,

 

KEYWORDS

Data Leakage Detection, Data Transformation, Weighted Graphs, L-Diversity, Improved Particle Swarm Optimization, Score Walk, Optimization.

 

ABSTRACT

Data leakage detection is as significant as data leakage prevention. It’s not simple to describe when one’s data has been compromised. Lots of businesses merely identify important leaks only after serious harm have to their clients and customers. Scenarios like these can ruin one’s business. Early data leakage detection can save millions of dollars and precious customers emotions. By means of this inspiration, weighted graphs are constructed based on the source documents in the previous work. Here terms are encrypted utilizing hash key function. Conventional cryptography techniques will not offer better security than the data anonymization methods. Consecutively, enhancement of the security of data is anticipated through the application l-diversity algorithm on the sensitive data. Sensitive data is anonymized through the utilization of the weighted graphs. By utilizing the label propagation algorithm, here an Improved Particle Swarm Optimization (IPSO) algorithm is used to generalize and conclude the correlations. Lastly, to describe the sensitivity, low-complexity score walk algorithm is employed. Evaluation outcomes will contrast the effectiveness of AWGPSOW with respect to ROC, precision, running and recall time. Experimental outcomes show that the anticipated technique will identify the fresh or leaks of the transformed data resourcefully and swiftly.

 

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