Secure Composition of Quantum Key Distribution and Symmetric Key Encryption
Bibliographic record
Abstract
Quantum key distribution (QKD) allows Alice and Bob to share a secret key with proven information-theoretic security. Composability-based security proofs for QKD ensure that using the established key to encrypt a message using a one-time-pad encryption system, will result in information-theoretic secrecy for the communication. In this paper, we consider the problem of using a QKD established key with a secure symmetrickey based encryption system with computational security, and use an extension of the framework of hybrid encryption to prove security of the composition. We use an extension of the original framework of hybrid encryption to correlated randomness setting (Sharifian et al. in ISIT 2021) and propose a quantum-enabled Key Encapsulation Mechanism (qKEM) that is used to construct a quantum-enabled hybrid encryption ($q H E$) system, and prove a composition theorem for the security of the qHE. We construct a qKEM with proven security using an existing QKD (Portmann et al. in Rev. of Mod. Physics 2022). Using this qKEM with a secure Data Encapsulation Mechanism (DEM), that can be constructed using a one-time symmetric key encryption scheme, results in an efficient encryption scheme for unrestricted length messages with proved security against an adversary with access to (efficient) quantum computations. Thus a key-efficient post-quantum secure encryption with proved security and without using any computational assumptions.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.005 |
| 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".