Encryption in phase space for classical coherent optical communications
Bibliographic record
Abstract
Optical layer attacks on optical fiber communication networks are one of the weakest reinforced areas of the network, allowing attackers to overcome security software or firewalls when proper safeguards are not put into place. Encrypting data using a random phase mask is a simple yet effective way to bolster the data security at the physical layer. Since the interactions of the random phases used for such encryption heavily depend on system properties like data rate, modulation format, distance, degree of phase randomness, laser properties, etc., it is important to determine the optimum operating conditions for different scenarios. In this work, assuming that the transmitter and the receiver have a secret pre-shared key, we present a theoretical study of security in such a system through mutual information analysis. Next, we determine operating conditions which ensure security for 4-PSK, 16-PSK, and 128-QAM formats through numerical simulation. Moreover, we provide an experimental demonstration of the system using 16-QAM modulation. We then use numerical simulation to verify the efficacy of the encryption and study two preventative measures for different modulation formats which will prevent an eavesdropper from obtaining any data. The results demonstrate that the system is secure against a tapping attack if an attacker has no information of the phase modulator and pre-shared key.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".