Security of quantum key distribution with iterative sifting
Bibliographic record
Abstract
Abstract Several quantum key distribution (QKD) protocols employ iterative sifting. After each quantum transmission round, Alice and Bob disclose part of their setting information (including their basis choices) for the detected signals. This quantum phase then ends when the basis dependent termination conditions are met, i.e., the numbers of detected signals per basis exceed certain pre-agreed threshold values. Recently, however, Pfister et al (2016 New J. Phys. 18 053001) showed that the basis dependent termination condition makes QKD insecure, especially in the finite key regime, and they suggested to disclose all the setting information after finishing the quantum phase. However, this protocol has two main drawbacks: it requires that Alice possesses a large memory, and she also needs to have some a priori knowledge about the transmission rate of the quantum channel. Here we solve these two problems by introducing a basis-independent termination condition to the iterative sifting in the finite key regime. The use of this condition, in combination with Azuma’s inequality, provides a precise estimation on the amount of privacy amplification that needs to be applied, thus leading to the security of QKD protocols, including the loss-tolerant protocol (Tamaki et al 2014 Phys. Rev. A 90 052314), with iterative sifting. Our analysis indicates that to announce the basis information after each quantum transmission round does not compromise the key generation rate of the loss-tolerant protocol. Our result allows the implementation of wider classes of classical post-processing techniques in QKD with quantified security.
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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".