On Securing Sensitive Data Using Deep Convolutional Autoencoders
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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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".