scholarly journals Hierarchical Infrastructure for Large-Scale Distributed Privacy-Preserving Data Mining

2005 ◽  
pp. 1020-1023 ◽  
Author(s):  
Jinlong Wang ◽  
Congfu Xu ◽  
Huifeng Shen ◽  
Yunhe Pan
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining

Accurate and large-scale privacy-preserving data mining using the election paradigm

2009 ◽  
Vol 68 (11) ◽  
pp. 1224-1236 ◽  
Author(s):  
Emmanouil Magkos ◽  
Manolis Maragoudakis ◽  
Vassilis Chrissikopoulos ◽  
Stefanos Gritzalis
Keyword(s):  
Data Mining ◽  
Large Scale ◽  
Privacy Preserving ◽  
Privacy Preserving Data Mining

A Privacy Preserving Data Mining Methodology for Dynamically Predicting Emerging Human Threats

2013 ◽  
Author(s):  
Gautam Manohar ◽  
Conrad Tucker
Keyword(s):  
Data Mining ◽  
Public Space ◽  
Real Time ◽  
Large Scale ◽  
Body Movement ◽  
Privacy Preserving ◽  
Emergent Behavior ◽  
Coordinate Space ◽  
Surveillance Systems ◽  
Privacy Preserving Data Mining

This paper proposes a privacy preserving data mining driven methodology for predicting emerging human threats in a public space by capturing large scale, real time body movement data (spatial data represented in X, Y, Z coordinate space) using Red-Green-Blue (RGB) image, infrared depth and skeletal image sensing technology. Unlike traditional passive surveillance systems (e.g., CCTV video surveillance systems), multimodal surveillance technologies have the ability to capture multiple data streams in a real time dynamic manner. However, mathematical models based on machine learning principles are needed to convert the large-scale data into knowledge to serve as a decision support system for autonomously predicting emerging threats, rather than just recording and observing them as they occur. To this end, the authors of this work present a privacy preserving data mining driven methodology that captures emergent behavior of individuals in a public space and classifies them as a threat or not a threat, based on the underlying body movements through space and time. An audience in a public environment is presented as the case study for this paper with the aim of classifying individuals in the audience as threats (or not), based on their temporal body behavior profiles.

A Perturbation Method Based on Singular Value Decomposition and Feature Selection for Privacy Preserving Data Mining

2014 ◽  
Vol 10 (1) ◽  
pp. 55-76 ◽  
Author(s):  
Mohammad Reza Keyvanpour ◽  
Somayyeh Seifi Moradi
Keyword(s):  
Data Mining ◽  
Feature Selection ◽  
Singular Value Decomposition ◽  
Perturbation Method ◽  
Privacy Preserving ◽  
Singular Value ◽  
Privacy Preserving Data Mining ◽  
Selection For ◽  
Value Decomposition ◽  
Different Levels

In this study, a new model is provided for customized privacy in privacy preserving data mining in which the data owners define different levels for privacy for different features. Additionally, in order to improve perturbation methods, a method combined of singular value decomposition (SVD) and feature selection methods is defined so as to benefit from the advantages of both domains. Also, to assess the amount of distortion created by the proposed perturbation method, new distortion criteria are defined in which the amount of created distortion in the process of feature selection is considered based on the value of privacy in each feature. Different tests and results analysis show that offered method based on this model compared to previous approaches, caused the improved privacy, accuracy of mining results and efficiency of privacy preserving data mining systems.

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