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.
Recommended Citation
Wang, Qing, "Human Action Image Generation With Differential Privacy" (2020). LSU Master's Theses. 5105.
https://repository.lsu.edu/gradschool_theses/5105
Committee Chair
Sun Mingxuan
DOI
10.31390/gradschool_theses.5105