Security of high speed quantum key distribution with finite detector dead time
Bibliographic record
Abstract
The security of a high speed quantum key distribution system with finite detector dead time $\tau$ is analyzed. When the transmission rate becomes higher than the maximum count rate of the individual detectors ($1/\tau$), security issues affect the scheme for sifting bits. Analytical calculations and numerical simulations of the Bennett-Brassard BB84 protocol are performed. We study Rogers et al.'s scheme (further information is available in [D. J. Rogers, J. C. Bienfang, A. Nakassis, H. Xu, and C. W. Clark, New J. Phys.~{\bf 9}, 319 (2007)]) in the presence of an active eavesdropper Eve who has the power to perform an intercept-resend attack. It is shown that Rogers et al.'s scheme is no longer guaranteed to be secure. More specifically, Eve can induce a basis-dependent detection efficiency at the receiver's end. Modified key sifting schemes that are basis-independent and thus secure in the presence of dead time and an active eavesdropper are then introduced. We analyze and compare these secure sifting schemes for this active Eve scenario, and calculate and simulate their key generation rate. It is shown that the maximum key generation rate is $1/(2\tau)$ for passive basis selection, and $1/\tau$ for active basis selection. The security analysis for finite detector dead time is also extended to the decoy state BB84 protocol for one particular secure sifting scheme.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".