Bibliographic record
Abstract
Encryptions are used in almost all standards to ensure the confidentiality of the data. Encryptions can be and indeed are implemented in the different layers of a network protocol stack. Conventional encryption performs the bitwise XOR operation between one message bit and one key stream bit to generate one ciphertext bit. Huo et al. have recently proposed to provide confidentialities on the user data by performing the phase encryption on the time domain OFDM samples in LTE system. Phase encryption is performed on the modulated symbols, different from the bit level of XOR encryption, i.e., stream cipher encryption. In this paper, we extend their study. We first generalize the phase encryption to general communication systems independent of the underlying modulation scheme. Then, we formulate the mathematical models for XOR and phase encryptions. Based on our model, we compare these two encryption methods in terms of their security and encryption efficiency. We also show phase encryption can resist traffic analysis attack when implemented in the physical layer. Finally, we conduct simulations to compare the performance of these two methods in terms of their decoding symbol error rate.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.008 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".