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Open AccessJournal ArticleDOI

Preserving Sensitive Information using Fuzzy C-Means Approach

Asha Kiran, +2 more
- 14 Aug 2018 - 
- Vol. 181, Iss: 10, pp 40-46
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.

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Citations
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An optimal (∊,δ)-differentially private learning of distributed deep fuzzy models

TL;DR: This paper suggests an optimal ( ∊, δ ) - differentially private noise adding mechanism that results in multi-fold reduction in noise magnitude over the classical Gaussian mechanism and thus leads to an increased utility for a given level of privacy.
Journal ArticleDOI

A Robust Approach to Secure Structured Sensitive Data using Non-Deterministic Random Replacement Algorithm

TL;DR: Comparison of proposed dynamic data masking model with existing masking methods shows that proposed model is would be superior in terms of sensitive data discovery, dynamic data masksing and data security.
References
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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

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.
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