Hybrid Encryption in Correlated Randomness Model
Bibliographic record
Abstract
A hybrid encryption scheme uses a key encapsulation mechanism (KEM) to generate and establish a shared secret key with the decrypter, and a secret key data encapsulation mechanism (DEM) to encrypt the data using the key that is established by the DEM. The decrypter recovers the key using the ciphertext that is generated by the KEM, and uses it to decrypt the ciphertext that is generated by the DEM.We motivate and propose hybrid encryption in correlated randomness model where all participants including the eavesdropper, have access to samples of their respective correlated random variables. We define information-theoretic KEM (iKEM), and prove a composition theorem for iKEM and DEM that allows us to construct an efficient hybrid encryption system in correlated randomness model, providing post-quantum security. The construction uses an information-theoretic one-way secret key agreement (OW-SKA) protocol that satisfies a new security definition, and a one-time symmetric key encryption system that can be implemented by XORing the output of a (computationally) secure pseudorandom generator with the message. We discuss our results and directions for future work.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".