Use of Comprehensive Technique for Preserving Privacy in Data Mining


Authors : Namrata Govind Ambekar; Rajyalakshmi Jaiswal

Volume/Issue : Volume 6 - 2021, Issue 6 - June

Google Scholar : http://bitly.ws/9nMw

Scribd : https://bit.ly/3qx5gNE

The world has entered into the digital age of information. Immersion in the field of information and technology comforts humanity but individual’s privacy and security is deteriorates. The private data provided by the individual and various organizations at the time of using mobile phone internet for different purposes, which may contain individuals sensitive information cannot be disclosed to the anonymous person without applying the privacy-preserving technique on it. Nowadays, Preserving Privacy Data Mining (PPDM) has been studied rigorously because of the wide penetration of sensitive information on the internet. Many techniques have been proposed so far like Kanonymization, l-diversity, Randomization, Perturbation methods, and Cryptographic techniques designed for Preserving Privacy Data Mining (PPDM). There are some plus and minus points of every approach. The negative point constitutes a loss of data, reduction in the utility of data, lack of diversity of data, security issues likewise. In this research work, we are going to propose a “Comprehensive Technique” which works amongst existing algorithm by analyzing some work done in this field. We proposed a novel technique named “Clustering Based Anonymization by Assigning Weight to Each attribute”, this k-means clustering algorithm is used with some of the alterations for anonymization of data. We are assigning feature weight manually so that distortion of data can be reduced. The main goal of the proposed model is to preserve privacy at the same time with minimum information loss.

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