Semester of Graduation

Spring 2020

Degree

Master of Science (MS)

Department

Computer Science and Engineering

Document Type

Thesis

Abstract

Large volumes of human action image data are becoming increasingly available due to the prevalence of surveillance cameras and smart personal devices. While such image data enables important applications such as activity recognition for health and safety enhancement, they often contain sensitive information such as identities that introduce high risks to individual privacy.

Existing image privacy-enhancing techniques are either developed at the cost of sacrificing image utility or lack of provable privacy guarantees. We propose a novel human action image generation model that enforces rigorous differential privacy protection. Theoretical analysis is provided to quantify the privacy protection on the training data within the differential privacy framework. Experiments with real-world datasets demonstrate that images generated using our method achieve higher image utilities than baselines given similar degrees of privacy protection.

Committee Chair

Sun Mingxuan

Available for download on Monday, March 15, 2027

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