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Perturbation based privacy for time-series data

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Title: Perturbation based privacy for time-series data
Author(s): Pham, Nam D.
Director of Research: Abdelzaher, Tarek F.
Doctoral Committee Chair(s): Abdelzaher, Tarek F.
Doctoral Committee Member(s): Blahut, Richard E.; Vaidya, Nitin H.; Borisov, Nikita
Department / Program: Electrical & Computer Eng
Discipline: Electrical & Computer Engr
Degree Granting Institution: University of Illinois at Urbana-Champaign
Degree: Ph.D.
Genre: Dissertation
Subject(s): Privacy Time-Series Information Theory Optimization
Abstract: This work is motivated by the emergence of participatory sensing applications, a new sensing paradigm that off-loads sensing responsibility from infrastructure sensors and professional sources to the crowd. This leads to unprecedented opportunities for sensory data collection and sharing. The privacy challenges in these applications arise naturally as personal data are shared among untrusted entities in the community. This dissertation develops mathematical foundations for optimal perturbation of both single-dimensional and multidimensional time-series data. The developed perturbation techniques allow users to effectively hide their original data while aggregated community statistics are still accurately reconstructed. Several real-world applications are also developed and successfully deployed that affirm the efficiency and accuracy of the perturbation and reconstruction techniques developed in this dissertation.
Issue Date: 2011-01-14
URI: http://hdl.handle.net/2142/18474
Rights Information: Copyright 2010 Nam D. Pham
Date Available in IDEALS: 2011-01-14
Date Deposited: December 2
 

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