Date of Award

12-31-2016

Document Type

Campus Access Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

Computer Science

First Advisor

Gabriel Ghinita

Second Advisor

Dan Simovici

Third Advisor

Ming Ouyang

Abstract

The past decade has witnessed tremendous growth in the mobile computing arena. Mobile users are able to participate in exciting social media applications, receiving information tailored to their location. However, there are growing privacy concerns that hinder further development of location-based applications as raw location data can disclose sensitive information about individuals. This thesis presents solutions for privacy-preserving location-processing algorithms. Both online and offline scenarios are considered: Two main directions are in the context of differential privacy, the de facto standard for statistical privacy-preserving data release: privately analyzing crowdsourced data collected by the users and extracting significant patterns from user trajectories. A third direction considers the case of online, non-statistical queries and our focus is to optimize a private information retrieval protocol by using GPU programming. The proposed solutions achieve strong protection, are computationally efficient and maintain high data accuracy, sometimes by more than an order of magnitude compared to competitive approaches.

Comments

Free and open access to this Campus Access Dissertation is made available to the UMass Boston community by ScholarWorks at UMass Boston. Those not on campus and those without a UMass Boston campus username and password may gain access to this dissertation through resources like Proquest Dissertations & Theses Global or through Interlibrary Loan. If you have a UMass Boston campus username and password and would like to download this work from off-campus, click on the "Off-Campus UMass Boston Users" link above.

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