Date of Award

5-2019

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Xiaohui Liang

Second Advisor

Dan Simovici

Third Advisor

Bo Sheng

Abstract

Sharing personal information has become a well-accepted norm for receiving customized and individualized services from the service providers in the cloud. However, sharing of such private and personal information may raise severe privacy violation issues. For example, Autonomous Vehicles (AV) have the potential to fundamentally alter the current transportation systems by making vehicle sharing possible, easy, and affordable. Electronic (eHealth) and Mobile (mHealth) healthcare systems have removed the physical barriers of traditional healthcare systems and made instant and continues patient data collection and sharing possible. These services can only be made possible by sharing of personal information, such as user location data and patient health information, which both are considered highly privacy sensitive. In the first part of this dissertation, we investigate the privacy preservation issues raised by such services, and provide solutions for them. Specifically, we propose a privacy preserving task matching and scheduling framework which makes sharing of the AVs possible without leaking any user location information. For mHealth system, we propose schemes for privacy preserving patient-caregiver communications, and deferentially private patient data release. In the second part, motivated by the dramatic growth in the number of smart mobile devices such as smartphones, and the high amount of sensitive and personal information they contained, we study user-oriented security by protecting smartphones from Lunch-time and Theft attacks. Specifically, we model these attacks using sensing and analysis of wireless signals and propose solutions for detecting and preventing them. Our evaluations on collected and simulated data confirm the effectiveness of our proposed schemes.

Comments

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