AN INTEGRATED TECHNIQUE IN ACHIEVING THECONFIDENTIALITY, INTEGRITY AND ROBUSTNESS FOR BIGDATA TRANSMISSION
Bibliographic record
Abstract
The data confidentiality, integrity, and data loss are issues during transmission due to inadequate security scheme. These issues become more critical in the big data transmission due to its own individual overhead. Moreover, multiple executions of distinct security algorithms for maintaining confidentiality and integrity reduce Throughput and add large number of additional bits as security overheads which hamper the robustness against data loss. Conversely, an efficient compression technique minimizes the data confidentiality as it eliminates the redundant data during compression. However, the current literature fails to suggest any security mechanism which can solve all these issues in a combinatorial way. Henceforth, this research addresses these security issues collectively without negatively affecting each other. It increases confidentiality and offers a backup for accidental data loss by combining Simplified Encryption Standard (SDES) and an advanced pattern generation technique which uses a unique pattern generation table. A novel dual round of error control technique has been incorporated to maximize the data integrity by addressing any number of transmission errors. A new compression technique is included to enhance robustness against data loss by producing high compression efficiency and resistance against transmission errors. Confidentiality and integrity are further enhanced by integrating an advance audio steganography which uses a distinctive sample selection for hiding bits. Experiments are conducted using standard Calgary Corpuses, text files (up to 1 TB), and audio files to validate the objectives. The proposed integrated technique offers higher confidentiality level by producing higher Signal to Noise Ratio (60.79-60.91 dB) and Frequency Difference (0.37-0.27 Hz) than other related security techniques. It can protect different security attacks by offering higher Avalanche Effects (76.2%) and Entropy Value (7.77). It also offers higher integrity and robustness against data loss by contributing lower percentages of Information Loss (0.004-0.0009%) and Uncorrectable Error Rate (0.0096-0.0094%).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".