Differential Cryptanalysis of Two Joint Encryption and Error Correction Schemes
Bibliographic record
Abstract
In GLOBECOM'10, Adamo et. al. proposed an interesting encryption scheme, called Error Correction-Based Cipher (ECBC), working at the physical layer. This scheme, together with its ancestor, Secret Error Correcting Code (SECC), belongs to the family of Joint Encryption and Error Correction (JEEC), which combines error correction and data encryption as one process to enable efficient implementations. In this paper, we provide rigorous investigation on the security of ECBC and SECC to unveil their cryptographic strengths under chosen-plaintext attacks. For ECBC, we found a 3-stage differential-style attack, which breaks the scheme with O(k × 2deg(f)+ 2k) effort, where deg(f) is the degree of the core cryptographic function f. For SECC, we found a similar attack of complexity O(k × 2k+1). Both of the attacks are significantly improved from exhaustive search, e.g., O(22k+kn+n × 2k) for ECBC and O(2kn+ (k+n) × 2k) for SECC. In addition, we exhibit that f used in ECBC's implementation is particularly vulnerable to our attack, which allows the attacker to recover the secret generator matrix in O(1). To mitigate this vulnerability, we propose a secure yet lightweight construction of f achieving the maximum degree. Finally, the core part of our attack against ECBC has been implemented utilizing GPU acceleration and demonstrated on a cluster GPU instance provided by Amazon EC2. Experimental results confirm that the original implementation of ECBC scheme can be broken in (almost) constant time (<;0.4 second) regardless of k, whereas the ECBC scheme 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 | 0.007 |
| 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 | 0.002 |
| 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".