Preserving Sensitive Information using Fuzzy C-Means Approach

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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2018
Authors:
Asha Kiran Grandhi, Manimala Puri, S. Srinivasa Suresh
10.5120/ijca2018917656

Asha Kiran Grandhi, Manimala Puri and Srinivasa S Suresh. Preserving Sensitive Information using Fuzzy C-Means Approach. International Journal of Computer Applications 181(10):40-46, August 2018. BibTeX

@article{10.5120/ijca2018917656,
	author = {Asha Kiran Grandhi and Manimala Puri and S. Srinivasa Suresh},
	title = {Preserving Sensitive Information using Fuzzy C-Means Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {August 2018},
	volume = {181},
	number = {10},
	month = {Aug},
	year = {2018},
	issn = {0975-8887},
	pages = {40-46},
	numpages = {7},
	url = {http://www.ijcaonline.org/archives/volume181/number10/29812-2018917656},
	doi = {10.5120/ijca2018917656},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Privacy is one of the important issues now days as privacy is linked with multidimensional issues; security, sentiment, fear, emotions, threats etc. Protecting privacy is as much as data utilization. In this day and age, data is getting generated largely by various industries. Medical industry is one of them. Providing safe access controls and privacy preservation are the primary concerns in the development of medical applications. Medical data possess sensitive information. According to the author, privacy should be preserved at all levels; storage level, to view level to knowledge discovery level. At view level, very limited approaches are proposed to protect the privacy of the medical data. This paper implements Fuzzy C means approach to protect the sensitive data while viewing blood donor data online. In this paper, a sample blood donor records are extracted to categorize the data into high sensitive data and low sensitive data using fuzzy C means rules. Subsequently, the model teaches the underlying relations to perform categorization based on the input. This paper describes the experiment in view of privacy preserving data mining. The experiment is simulated using MATLAB and shows satisfactory result.

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Keywords

Sensitive data, Non sensitive data, confidential data, privacy preserving data mining, FCM algorithm.