Attacks exploiting deviation of mean photon number in quantum key distribution and coin tossing
Bibliographic record
Abstract
The security of quantum communication using a weak coherent source requires an accurate knowledge of the source's mean photon number. Finite calibration precision or an active manipulation by an attacker may cause the actual emitted photon number to deviate from the known value. We model effects of this deviation on the security of three quantum communication protocols: the Bennett-Brassard 1984 (BB84) quantum key distribution (QKD) protocol without decoy states, Scarani-Ac\'{\i}n-Ribordy-Gisin 2004 (SARG04) QKD protocol, and a coin-tossing protocol. For QKD we model both a strong attack using technology possible in principle and a realistic attack bounded by today's technology. To maintain the mean photon number in two-way systems, such as plug-and-play and relativistic quantum cryptography schemes, bright pulse energy incoming from the communication channel must be monitored. Implementation of a monitoring detector has largely been ignored so far, except for ID Quantique's commercial QKD system Clavis2. We scrutinize this implementation for security problems and show that designing a hack-proof pulse-energy-measuring detector is far from trivial. Indeed, the first implementation has three serious flaws confirmed experimentally, each of which may be exploited in a cleverly constructed Trojan-horse attack. We discuss requirements for a loophole-free implementation of the monitoring detector.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.009 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".