On Securing Sensitive Data Using Deep Convolutional Autoencoders
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There are various traditional methods used for securing sensitive data, such as cryptography algorithms like AES-HMAC-SHA256, Twofish, and Chacha20. However, several studies showed that these cryptography algorithms suffer from security vulnerabilities. In this paper, we explore the use of a cryptography model based on a Deep Convolutional Autoencoder and we compare its performances to the cryptography algorithms. We report the results of a comparative study based on several metrics. We incorporate more nuanced metrics such as cosine similarity, entropy, Kendall and Spearman rate, and Mean Squared Error (MSE) for a comprehensive assessment of model performance and security, in addition to encryption and decryption time metrics.The results obtained are very promising. Our model performs the best on two essential metrics, entropy and MSE. We obtain a decrypted file entropy of 8.01, compared to 7.99 for the three other standard models, with a very low MSE of 0.003, compared to 105.43 for AES, which remains the most efficient compared to the other algorithms.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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