Foiling covert channels and malicious classical post-processing units in quantum key distribution
Bibliographic record
Abstract
Abstract The existing paradigm for the security of quantum key distribution (QKD) suffers from two fundamental weaknesses. First, covert channels have emerged as an important threat and have attracted a lot of attention in security research in conventional information and communication systems. Covert channels (e.g. memory attacks) can fatally break the security of even device-independent quantum key distribution (DI-QKD), whenever QKD devices are re-used. Second, it is often implicitly assumed that the classical post-processing units of a QKD system are trusted. This is a rather strong assumption and is very hard to justify in practice. Here, we propose a new paradigm for the security of QKD that addresses these two fundamental problems. Specifically, we show that by using verifiable secret sharing and multiple optical devices and classical post-processing units, one could re-establish the security of QKD. Our techniques are rather general and they apply to both DI-QKD and non-DI-QKD.
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 | 0.007 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.003 | 0.010 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.007 |
| 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".