Protect biometric data with compound chaotic encryption
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
Abstract In this paper, the information security issue on biometric data is studied. We introduce the development of biometric technology with its application in security systems and discuss the significance. Focusing on distribution in space domain and uniform diffusion in frequency domain, a compounded chaotic cipher strategy with dynamic Bernoulli mapping is proposed to improve the performance on cryptographic text with consideration both of volatility and correlation. Aiming at visual original biometric data, related tests and analysis on key space, sensitivity, correlation, and uniform distribution are performed with comparison to diverse schemes including triple data encryption standard algorithm and logistic mapping cipher. Experiments results show that the proposed approach possesses good secure performances on both random scrambling in space domain and uniform distribution in frequency domain. The cryptosystem can be implemented with basic computational operators and provides an efficient and sensitive key space scheme for biometric data protection. Copyright © 2014 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Open science | 0.002 | 0.001 |
| 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