Anonymized Identity Tracking: Privacy Preserving Facial Encoding

Background: The need for sharing large-scale datasets in training deep learning 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.Method: 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 a publicly available facial database and focused on evaluating the trade-offs inherent in image encoding techniques, with a special emphasis on balancing privacy and model accuracy.Conclusion: This study navigate the balance between protecting sensitive data and meeting the data demands necessary for effective model training. It sheds light on the intricate trade-offs among different image encoding techniques and offers insights into finding an optimal balance between privacy protection and model performance.