Keyed masking for secure multi-user semantic communication

Reproducibility package for the TIFS submission: transmit and receive
core, security stages (eavesdropper, jamming, key families, attack
difficulty, known-plaintext), real BERT token streams, closed-form
verification, and the scripts that regenerate every figure and table
from the released CSVs.

README.md

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+# Keyed Masking for Secure Multi-User Semantic Communication
+
+Reproducibility package for the manuscript *Mask-as-Key Secure Multiple
+Access for Semantic Communications: Physical-Layer Encryption and
+Jamming Robustness*, submitted to the IEEE Transactions on Information
+Forensics and Security.
+
+Every figure and table in the paper is regenerated from this
+repository. Experiment scripts write CSV files to `data/` and never
+draw; `replot_security.py` reads only `data/` and writes the figure PDFs
+to `fig/`; `make_tables.py` prints the LaTeX rows of the result tables.
+
+## Idea
+
+Multi-user semantic communication superposes several users on one frame
+and multiplies each user embedding by a distinct pattern so that the
+receivers can separate them. This code treats that pattern as a secret
+key. One keyed operation then encrypts each user against a receiver
+without the key, separates the users, and spreads a jammer that does not
+hold the key, at no extra bandwidth, power, or rate.
+
+## Layout
+
+```
+code/
+  sse_lib.py            transmit and receive core, channel, training, OMA reference
+  exp_full.py           stages A-F: SNR sweep, key length, jamming, key families,
+                        scheme comparison, attack difficulty
+  exp_kpa.py            stage H: known-plaintext attack on the key
+  exp_real_sec.py       stage G: real BERT WordPiece token streams
+  verify_math.py        closed-form checks V1-V5 against Monte Carlo, PASS/FAIL
+  replot_security.py    every result figure, from data/ to fig/
+  make_tables.py        LaTeX rows of the two result tables, from data/
+  feasibility_security.py   early CPU-sized study, kept for the record
+data/                   CSV results, one file per stage
+fig/                    figure PDFs, regenerated by replot_security.py
+```
+
+## Reproducing
+
+Requires Python 3, PyTorch, NumPy, and Matplotlib. The real-token stage
+additionally needs `datasets` and `transformers`. A CUDA device is
+recommended; the code falls back to CPU. Under Windows install PyTorch
+in WSL, because the native Windows build does not load the CUDA
+libraries.
+
+```bash
+python verify_math.py        # closed-form verification, prints PASS/FAIL
+python exp_full.py           # stages A-F
+python exp_kpa.py            # known-plaintext attack
+python exp_real_sec.py       # real token streams
+python replot_security.py    # all figures from the CSVs
+python make_tables.py        # LaTeX rows of the result tables
+```
+
+Seeds are fixed: training 1, evaluation 777, attacker key guess
+20260813. Re-running reproduces the released CSV files.
+
+## Figure and table map
+
+| Artifact | Script | Data |
+|---|---|---|
+| Fig. 2 SER against SNR | `exp_full.stage_A` | `sec_snr.csv` |
+| Fig. 3 key length | `exp_full.stage_B` | `sec_keylen.csv` |
+| Fig. 4 jamming | `exp_full.stage_C` | `sec_jam.csv` |
+| Fig. 5 key sensitivity | `exp_full.stage_F` | `sec_sens.csv` |
+| Fig. 6 brute-force search | `exp_full.stage_F` | `sec_brute.csv` |
+| Fig. 7 known-plaintext attack | `exp_kpa` | `kpa.csv` |
+| Fig. 8 real token streams | `exp_real_sec` | `real_sec_ter.csv` |
+| Scheme comparison table | `exp_full.stage_E` | `sec_compare.csv` |
+| Key family table | `exp_full.stage_D` | `sec_maskfam.csv`, `sec_regjam.csv` |
+| Headline recovery table | `exp_real_sec` | `real_sec_stats.json` |
+
+## Security scope
+
+The analysis covers an adversary that observes transmitted frames. The
+masking is linear, so an adversary that also learns the indices some
+frames carried recovers the key from a few frames, which `exp_kpa.py`
+measures. The key must therefore be refreshed per coherence block from
+a shared seed, as the paper states. This repository implements the
+measurement of that limit, not a key-refresh layer.
+
+## License
+
+MIT for the code. The AG News data and the language-model tokenizer are
+obtained from their own sources under their own terms.