On the (in)security of two Joint Encryption and Error Correction schemes
Bibliographic record
Abstract
Joint Encryption and Error Correction (JEEC) is proposed to combine encoding/encryption as one process to boost more compact implementations. In this paper, we provide rigorous investigation on the security of two JECC schemes, namely ECBC and SECC. For ECBC, we found a 3–stage differential–like attack, which breaks it with O(k × 2deg(f) + 2k) effort, where deg(f) is the degree of the core cryptographic function f and k is the block length. For SECC, we found a similar attack of complexity O(k × 2k+1). Additionally, we exhibit that f used in ECBC is particularly vulnerable, which allows the secret matrix to be recovered in O(1). To mitigate this vulnerability, we propose a secure–yet–lightweight construction of f. Finally, the core part of our attack has been implemented. Experimental results confirm that the original implementation of ECBC can be broken in constant time (<0.4 s) regardless of k, whereas the ECBC enhanced by our proposed f can withstand this attack to the maximum extent.
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.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".