Enhancing Privacy Protection in Online Federated Learning: A Method for Secure Face Image De-Identification Using a Modified Diffie-Hellman Algorithm
Why this work is in the frame
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Bibliographic record
Abstract
The proliferation of face images, alongside their widespread dissemination and easy accessibility through social media, underscores a pressing challenge to personal identification information protection.Conversely, advancements in identity-agnostic computer vision technologies offer valuable benefits, necessitating cautious utilization of face images to safeguard individual privacy.'Face de-identification', or 'face anonymization', refers to the process of altering an original face image to a nearidentical one that obscures the subject's actual identity.Existing de-identification strategies, despite considerable efforts, often fall short in photo-realism or fail to strike an optimal balance between privacy and utility.This study proposes an approach for generating de-identified facial images using instances, addressing the potential privacy breaches and identity exposure associated with facial features.The proposed system involves a two-stage training process.Initially, a federated learning framework is suggested, enabling knowledge amalgamation through the mutual exchange of model parameters among clients during federated training, devoid of data sharing.Subsequently, sensitive information is secured using an enhanced version of the Diffie-Hellman algorithm coupled with a genetic algorithm.In the event of data loss or corruption, an optimized genetic algorithm (OGA) is employed to successfully restore the data, thereby offering protection against potential insider threats in federated learning.The decryption process is then executed as if the user had initiated the request.Experimental results demonstrate that the proposed federated learning approach delivers performance equivalent to centralized learning, thereby validating the practicality and effectiveness of the suggested architecture.Specifically, a model of the federated learning-deep convolutional neural network (FL-DCNN) achieved an accuracy of 95.2%, precision and F1-score of 95%, recall of 96%, and a final specificity of 96.80%.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it