Testing Random-Detector-Efficiency Countermeasure in a Commercial System Reveals a Breakable Unrealistic Assumption
Bibliographic record
Abstract
In the last decade, efforts have been made to reconcile theoretical security with realistic imperfect implementations of quantum key distribution (QKD). Implementable countermeasures are proposed to patch the discovered loopholes. However, certain countermeasures are not as robust as would be expected. In this paper, we present a concrete example of ID Quantique's random-detector-efficiency countermeasure against detector blinding attacks. As a third-party tester, we have found that the first industrial implementation of this countermeasure is effective against the original blinding attack, but not immune to a modified blinding attack. Then, we implement and test a later full version of this countermeasure containing a security proof [C. C. W. Lim et al., IEEE Journal of Selected Topics in Quantum Electronics, 21, 6601305 (2015)]. We find that it is still vulnerable against the modified blinding attack, because an assumption about hardware characteristics on which the proof relies fails in practice.
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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 | 0.009 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.007 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".