MSK‐PK: A Public‐Key Encryption Cryptosystem with Multiple Secret‐Keys
Bibliographic record
Abstract
By allowing intermediate nodes to combine multiple packets before forwarding them, the concept of network coding in multi-cast networks can provide maximum possible information flow. However, this also means traditional encryption methods are less applicable, since the different public-keys of receivers imply different ciphertexts which cannot be easily combined by network coding. While network coding itself may provide confidentiality, its effectiveness heavily depends on the underlying network topology and ability of the eavesdroppers. Finally, broadcast encryption and group key agreement techniques both allow a sender to broadcast the same ciphertext to all the receivers, although they rely on the assumptions of trusted key servers or secure channels. In this paper, we propose a novel public-key encryption concept with a single public-key for encryption and multiple secret keys for decryption (MSK-PK), which has limited ciphertext expansion and does not require trusted key servers or secure channels. To demonstrate the feasibility of this concept, we construct a concrete scheme based on a class of lattice-based multi-trapdoor functions. We prove that those functions satisfy the one-wayness property and can resist the nearest plane algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| 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) | 0.002 | 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".