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Toward Privacy in High-Dimensional Data Publishing


Toward Privacy in High-Dimensional Data Publishing

Chen, Rui (2012) Toward Privacy in High-Dimensional Data Publishing. PhD thesis, Concordia University.

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Nowadays data sharing among multiple parties has become inevitable in various application domains for diverse reasons, such as decision support, policy development and data mining. Yet, data in its raw format often contains person-specific sensitive information, and publishing such data without proper protection may jeopardize individual privacy. This fact has spawned extensive research on privacy-preserving data publishing (PPDP), which balances the fundamental trade-off between individual privacy and the utility of published data. Early research of PPDP focuses on protecting private and sensitive information in relational and statistical data. However, the recent prevalence of several emerging types of high-dimensional
data has rendered unique challenges that prevent traditional PPDP techniques from being directly used. In this thesis, we address the privacy concerns in publishing four types of high-dimensional data, namely set-valued data, trajectory data, sequential data and network data. We develop effective and efficient non-interactive data publishing solutions for various utility requirements. Most of our solutions satisfy a rigorous privacy guarantee known as differential privacy, which has been the de facto standard for privacy protection. This thesis demonstrates that our solutions have exhibited great promise for releasing useful high-dimensional data without endangering individual privacy.

Divisions:Concordia University > Gina Cody School of Engineering and Computer Science > Computer Science and Software Engineering
Item Type:Thesis (PhD)
Authors:Chen, Rui
Institution:Concordia University
Degree Name:Ph. D.
Program:Computer Science
Date:7 September 2012
Thesis Supervisor(s):Desai, Bipin C. and Fung, Benjamin C. M.
ID Code:974691
Deposited By: RUI CHEN
Deposited On:29 Oct 2012 19:45
Last Modified:18 Jan 2018 17:38
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