Privacy-Preserving Data Publishing

Privacy-Preserving Data Publishing
An Overview

Raymond Chi-Wing Wong, Ada Wai-Chee Fu
ISBN: 9781608452163 | PDF ISBN: 9781608452170
Copyright © 2010 | 138 Pages | Publication Date: 01/01/2010

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Privacy preservation has become a major issue in many data analysis applications. When a data set is released to other parties for data analysis, privacy-preserving techniques are often required to reduce the possibility of identifying sensitive information about individuals. For example, in medical data, sensitive information can be the fact that a particular patient suffers from HIV. In spatial data, sensitive information can be a specific location of an individual. In web surfing data, the information that a user browses certain websites may be considered sensitive. Consider a dataset containing some sensitive information is to be released to the public. In order to protect sensitive information, the simplest solution is not to disclose the information. However, this would be an overkill since it will hinder the process of data analysis over the data from which we can find interesting patterns. Moreover, in some applications, the data must be disclosed under the government regulations. Alternatively, the data owner can first modify the data such that the modified data can guarantee privacy and, at the same time, the modified data retains sufficient utility and can be released to other parties safely. This process is usually called as privacy-preserving data publishing. In this monograph, we study how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information.

Table of Contents: Introduction / Fundamental Concepts / One-Time Data Publishing / Multiple-Time Data Publishing / Graph Data / Other Data Types / Future Research Directions

Table of Contents

Introduction
Fundamental Concepts
One-Time Data Publishing
Multiple-Time Data Publishing
Graph Data
Other Data Types
Future Research Directions

About the Author(s)

Raymond Chi-Wing Wong, The Hong Kong University of Science and Technology

Ada Wai-Chee Fu, The Chinese University of Hong Kong

Related Series

Data Mining and Knowledge Discovery

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