Graduation Year

2014

Document Type

Dissertation

Degree

Ph.D

Degree Name

Doctor of Philosophy (Ph.D.)

Department

Computer Engineering

Degree Granting Department

Computer Science and Engineering

Major Professor

Miguel A. Labrador, Ph.D.

Co-Major Professor

Roberto Callarotti, Ph.D.

Committee Member

Roberto Callarotti, Ph.D.

Committee Member

Kenneth Christensen, Ph.D.

Committee Member

Wilfrido Moreno, Ph.D.

Committee Member

Rafael Perez, Ph.D.

Keywords

Anonymization, Encryption, Energy Consumption, Obfuscation, Quality of Information

Abstract

Participatory Sensing (PS) is a new data collection paradigm in which people use their cellular phone resources to sense and transmit data of interest to address a collective problem that would have been very difficult to assess otherwise. Although many PS-based applications can be foreseen to solve interesting and useful problems, many of them have not been fully implemented due to privacy concerns. As a result, several privacy-preserving mechanisms have been proposed. This dissertation presents the state-of-the-art of privacy-preserving mechanisms for PS systems. It includes a new taxonomy and describes the most important issues in the design, implementation, and evaluation of privacy-preserving mechanisms. Then, the most important mechanisms available in the literature are described, classified and qualitatively evaluated based on design issues. The dissertation also presents a model to study the interactions between privacy-preserving, incentive and inference mechanisms and the effects that they may have on one another, and more importantly, on the quality of information that the system provides to the final user.

Then, a new hybrid privacy-preserving mechanism is proposed. This algorithm dynamically divides the area of interest into cells of different sizes according to the variability of the variable of interest being measured and chooses between two privacy-preserving mechanisms depending on the size of the cell. In small cells, where participants can be identified easier, the algorithm uses a double-encryption technique to protect the privacy of the participants and increase the quality of the information. In bigger cells, where the variability of the variable of interest is low, data anonymization and obfuscation techniques are used to protect the actual location (privacy) of the participant. The proposed mechanism is evaluated along with other privacy-preserving mechanisms using a real PS system for air pollution monitoring. The systems are evaluated considering the quality of information provided to the final user, energy consumption, and the level of privacy protection. This last criterion is evaluated when the adversary is able to compromise one or several records in the system. The experiments show the superior performance of proposed mechanism and the existing trade-offs in terms of privacy, quality of information, and energy consumption.

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