Preserving Sensitive Information using Fuzzy C-Means Approach
TLDR
Fuzzy C means approach is implemented to protect the sensitive data while viewing blood donor data online using fuzzy C means rules.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.read more
Citations
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References
More filters
Journal ArticleDOI
Top 10 algorithms in data mining
Xindong Wu,Vipin Kumar,J. Ross Quinlan,Joydeep Ghosh,Qiang Yang,Hiroshi Motoda,Geoffrey J. McLachlan,Angus S. K. Ng,Bing Liu,Philip S. Yu,Zhi-Hua Zhou,Michael Steinbach,David J. Hand,Dan Steinberg +13 more
TL;DR: This paper presents the top 10 data mining algorithms identified by the IEEE International Conference on Data Mining (ICDM) in December 2006: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART.
Proceedings ArticleDOI
Limiting privacy breaches in privacy preserving data mining
TL;DR: This paper presents a new formulation of privacy breaches, together with a methodology, "amplification", for limiting them, and instantiate this methodology for the problem of mining association rules, and modify the algorithm from [9] to limit privacy breaches without knowledge of the data distribution.
Proceedings ArticleDOI
Privacy preserving mining of association rules
TL;DR: A class of randomization operators are proposed that are much more effective than uniform randomization in limiting the breaches of privacy breaches and derived formulae for an unbiased support estimator and its variance are derived.
Journal ArticleDOI
State-of-the-art in privacy preserving data mining
Vassilios S. Verykios,Elisa Bertino,Igor Nai Fovino,Loredana Parasiliti Provenza,Yucel Saygin,Yannis Theodoridis +5 more
TL;DR: An overview of the new and rapidly emerging research area of privacy preserving data mining is provided, and a classification hierarchy that sets the basis for analyzing the work which has been performed in this context is proposed.
Book ChapterDOI
K-anonymous data mining : a survey
TL;DR: This chapter discusses how the privacy requirements characterized by k-anonymity can be violated in data mining and introduces possible approaches to ensure the satisfaction of k-Anonymity in datamining.