Low Dimensional Data Privacy Preservation Using Multi Layer Artificial Neural Network

Low Dimensional Data Privacy Preservation Using Multi Layer Artificial Neural Network

R. VidyaBanu (Sri Krishna College of Engineering and Technology, India) and N. Nagaveni (Coimbatore Institute of Technology, India)
Copyright: © 2012 |Pages: 15
DOI: 10.4018/jiit.2012070102
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Government agencies, business enterprises and non-profit organizations are searching for innovative methods to collect and analyze data about individuals or businesses to support their decision making processes. Data mining techniques are able to derive sensitive knowledge from unclassified data, causing a severe threat to privacy. The authors provide a promising solution to address the demand for privacy preservation in clustering analysis. They propose a novel dimensionality expansion based data privacy preservation technique using multi-layer artificial neural network. By applying this idea, the authors can project a low dimensional data into a high dimensional space to enhance the privacy level. Clustering was done using K-means and the results show that privacy level and the nature of data were very much preserved even after this transformation. The results arrived at were significant and the proposed method transformed the data better than the classical Geometric data transformation based methods.
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1. Introduction

Data mining and knowledge discovery in databases are two new-fangled research areas that explore the non trivial, mechanical extraction of implicit, previously unknown and potentially useful patterns from large volumes of data. Data mining is a technique of extrapolating useful information from a collection of data that can be used to predict future behavior. Data mining has been used extensively in the banking, health care, business and financial sectors to model and predict credit fraud, evaluate risk, perform trend analysis, profitability analysis, in stock-price forecasting, disease prediction (Moudani et al., 2012), option trading, bond rating, portfolio management, commodity price prediction, key phrase extraction from digital libraries (Qi et al., 2011), intelligent information retrieval (Veeramalai & Kannan, 2011), software effort estimation (Deng, 2011), document clustering (Sridevi & Nagaveni, 2011), comparison opinion (Xu et al., 2011), stock prediction in mergers and acquisitions, forecasting financial disasters etc. Large scale data collection has become easy due to swift growth of Internet and World Wide Web. This data may include private and sensitive information, which, when revealed to public can cause harm to data subjects, data owners, data users, or other relevant parties. With the advent of technology and internet, people voluntarily provide personal information to banks, hospitals, surveys, super markets, government, commercial organizations and social networking web-sites for different purposes without realizing that this information may cause serious threats to their privacy. In such cases, beyond the voluntary exchange of personal data, people are sometimes forced in some circumstances to surrender their personal data in order to gain something (Wright et al., 2009).They do not even have the right to opt out from the sharing their personal data. Thus, the burden of data privacy protection falls on the shoulder of the data holder (Fung et al., 2009). Privacy of personal data is a fundamental human right. The freedom and transparency of data flow due to swift advances in data processing techniques and internet technology has heightened concerns of privacy. Reluctance to provide personal information could impede the success of data mining. Although mining is expected to produce remarkable knowledge and uncover interesting patterns, it also creates a great threat to privacy. The data and knowledge might be potentially abused by people for their personal benefits. As a result, privacy issues are persistently under the attention and this may well intimidate the use of data mining and all its benefits. It is thus of enormous importance to develop sufficient safety measures and techniques for protecting the privacy of data used for mining. Some of the reasons that make privacy a concern are: laws regarding privacy that can restrict how people can mine data; moral issues involved in disseminating data; concerns of people about their personal information being shared. The difficulty arises of how mining can be done effectively while still preserving the privacy of data.

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