Author

Yifan Tian

Date of Award

11-2019

Document Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy in Electrical Engineering & Computer Science

Department

Electrical, Computer, Software, and Systems Engineering

Committee Chair

Jiawei Yuan, Ph.D.

First Committee Member

Radu Babiceanu, Ph.D.

Second Committee Member

Yantian Hou, Ph.D.

Third Committee Member

Remzi Seker, Ph.D.

Fourth Committee Member

Houbing Song, Ph.D.

Fifth Committee Member

Tianyu Yang, Ph.D.

Abstract

The widespread use of smartphones and camera-coupled Internet of Thing (IoT) devices triggers an explosive growth of imagery data. To extract and process the rich contents contained in imagery data, various image analysis techniques have been investigated and applied to a spectrum of application scenarios. In recent years, breakthroughs in deep learning have powered a new revolution for image analysis in terms of effectiveness with high resource consumption. Given the fact that most smartphones and IoT devices have limited computational capability and battery life, they are not ready for the processing of computational intensive analytics over imagery data collected by them, especially when deep learning is involved. To resolve the bottleneck of computation, storage, and energy for these resource constrained devices, offloading complex image analysis to public cloud computing platforms has become a promising trend in both academia and industry. However, an outstanding challenge with public cloud is on the protection of sensitive information contained in many imagery data, such as personal identities and financial data. Directly sending imagery data to the public cloud can cause serious privacy concerns and even legal issues.

In this dissertation, I propose a comprehensive privacy-preserving imagery data analysis framework which can be integrated in different application scenarios to assist image analysis for resource-constrained devices with efficiency, accuracy, and privacy protection. I first identify security challenges in the utilization of public cloud for image analysis. Then, I design and develop a set of novel solutions to address these challenges. These solutions will be featured by strong privacy guarantee, lightweight computation, low accuracy loss compared with image analysis without privacy protection. To optimize the communication overhead and resource utilization of using cloud computing, I investigate edge computing, which is a promising technique to ameliorate the high communication overhead in cloud-assisted architectures. Furthermore, to boost the performance of my solutions under both cloud and edge deployment, I also provide a set of pluggable enhancement modules to be applied to meet different requirements for various tasks. By exploring the features of edge computing and cloud computing, I flexibly incorporate them as a comprehensive framework to provide privacy-preserving image analysis services.

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