Perturbation based privacy for time-series data

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.

Name thesis:degree_name

Ph.D.

Level thesis:degree_level

Dissertation

Discipline thesis:degree_discipline

Electrical & Computer Engr

Grantor

University of Illinois at Urbana-Champaign

Year dc:date

2011

Author dc:creator

• Pham, Nam D.

Contributors dc:contributor

• Abdelzaher, Tarek F.
• Blahut, Richard E.
• Vaidya, Nitin H.
• Borisov, Nikita

• Privacy
• Time-Series
• Information Theory
• Optimization

Statement dc:rights

• Copyright 2010 Nam D. Pham

Language dc:language

en

Handle dc:identifier

http://hdl.handle.net/2142/18474

OAI identifier oai:identifier

oai:www.ideals.illinois.edu:2142/18474