Graduation Year
2024
Document Type
Thesis
Degree
M.S.C.S.
Degree Name
MS in Computer Science (M.S.C.S.)
Degree Granting Department
Computer Science and Engineering
Major Professor
Dmitry Goldgof, Ph.D.
Committee Member
Lawrence Hall, Ph.D.
Committee Member
Shaun Canavan, Ph.D.
Committee Member
Ghada Zamzmi, Ph.D.
Keywords
Differential Privacy, Facial De-identification, Feature Extraction
Abstract
The need for sharing large-scale datasets to train deep neural network models, particularly in healthcare, raises significant data security and privacy concerns. To address these issues, methods such as data encryption or encoding are utilized. These techniques can encrypt the data and make it unreadable to humans, while still retaining its usefulness for training models.
In this study, we investigate various image encoding techniques designed to protect privacy by making images unrecognizable while still retaining their usefulness for model training. Our investigation utilized publicly available facial databases and focused on evaluating the trade-offs inherent in image encoding techniques, with a special emphasis on balancing privacy and model accuracy.
This study navigates the balance between protecting sensitive data and meeting the data demands necessary for effective model training. It sheds light on the intricate tradeoffs among different image encoding techniques and offers insights into finding an optimal balance between privacy protection and model performance.
Scholar Commons Citation
Pakalapati, Manas Sanjay, "Anonymized Identity Recognition and Classification Using Privacy Preserving Facial Encoding" (2024). USF Tampa Graduate Theses and Dissertations.
https://digitalcommons.usf.edu/etd/10549
