KiHoLee
17d23fa76a
check_family_enum.py measures the attack the manuscript now states in Section III-A: the winning correlation is an index-free verifier, so ranking the 63 non-constant Walsh rows by mean winning correlation recovers the user set from one frame in 0.905 of 200 trials at 10 dB and from four frames in 0.990, using nothing outside the stated threat model. Under the invariance refresh it recovers it in none, because the entry permutation relabels the codebook the adversary must align against. V8 and V9 read the trained codebook through main_model(), which retrains on every call, and a codebook trained on CUDA is not the one trained on CPU. The shipped verify_math.csv therefore read PASS here and FAIL for anyone running this package without a GPU. model_main.pt is 7 KB and fixes the codebook, which is what both checks are about; delete it to retrain. V1-V11 now pass on both. New checks: V10, the format-matched OMA reference Section VI-B quotes, and V11, the closed-form against Monte Carlo comparison the manuscript claimed and never stored. V3a's bias-linearity result was computed and printed but never written to the CSV, so the one linearity claim the paper quotes was the one this package could not show. check_consistency.py gains 21 assertions, covering five data files that no assertion read (users, csi, semantic, cov_attack, sec_jam) and the trend claims it structurally could not see, since it compared values and not shapes. README: the figure map named stages that do not write the artifacts they list, so following it did not reproduce Figs. 4 and 6; the reproduction block was five scripts short; and the refresh numbers were from a superseded run (nearly three, 15.0 to 64.8 bits) against the manuscript's 2.3 and 23.8 to 364.6.
120 lines
5.8 KiB
Markdown
120 lines
5.8 KiB
Markdown
# Keyed Masking for Secure Multi-User Semantic Communication
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Reproducibility package for the manuscript *Mask-as-Key Secure Multiple
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Access for Semantic Communications: Physical-Layer Encryption and
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Jamming Robustness*, submitted to the IEEE Transactions on Information
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Forensics and Security.
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Every figure and table in the paper is regenerated from this
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repository. Experiment scripts write CSV files to `data/` and never
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draw; `replot_security.py` reads only `data/` and writes the figure PDFs
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to `fig/`; `make_tables.py` prints the LaTeX rows of the result tables.
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## Idea
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Multi-user semantic communication superposes several users on one frame
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and multiplies each user embedding by a distinct pattern so that the
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receivers can separate them. This code treats that pattern as a secret
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key. One keyed operation then encrypts each user against a receiver
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without the key, separates the users, and spreads a jammer that does not
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hold the key, at no extra bandwidth, power, or rate.
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## Layout
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```
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code/
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sse_lib.py transmit and receive core, channel, training, OMA reference
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exp_full.py stages A-F and L: SNR sweep, key length, jamming across
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schemes, key families, scheme comparison, attack difficulty
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exp_kpa.py stage H: known-plaintext attack on the key
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exp_refresh.py stage K: the key-refresh layer, invariance group
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exp_permkpa.py permutation-key known-plaintext attack (Fig. 7)
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check_cov_*.py ciphertext-only covariance-attack checks (referee M1)
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exp_real_sec.py stage G: real BERT WordPiece token streams
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verify_math.py closed-form checks V1-V11, PASS/FAIL and verify_math.csv
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replot_security.py every result figure, from data/ to fig/
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make_tables.py LaTeX rows of every result table, from data/
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feasibility_security.py early CPU-sized study, kept for the record
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data/ CSV results, one file per stage
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fig/ figure PDFs, regenerated by replot_security.py
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```
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## Reproducing
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Requires Python 3, PyTorch, NumPy, and Matplotlib. The real-token stage
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additionally needs `datasets` and `transformers`. A CUDA device is
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recommended; the code falls back to CPU. Under Windows install PyTorch
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in WSL, because the native Windows build does not load the CUDA
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libraries.
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```bash
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python verify_math.py # closed-form verification, prints PASS/FAIL
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python exp_full.py # stages A-F and L
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python exp_kpa.py # known-plaintext attack
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python exp_refresh.py # the key-refresh layer
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python exp_real_sec.py # real token streams
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python exp_permkpa.py # permutation-key known plaintext
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python exp_infotheory.py # mutual information and equivocation
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python exp_semantic.py # semantic-similarity leakage
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python exp_users_csi.py # load and channel-estimate sweeps
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python check_cov_attack.py # ciphertext-only covariance attack
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python check_family_enum.py # ciphertext-only enumeration of the key family
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python replot_security.py # all figures from the CSVs
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python make_tables.py # LaTeX rows of the result tables
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```
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Seeds are fixed: training 1, evaluation 777, attacker key guess
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20260813, key recovery 4242, brute-force search 31, cross-scheme
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comparison 11, key refresh 5150. Re-running reproduces the released CSV
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files.
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## Conventions
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Flat Rayleigh fading, one gain per user per frame, with unit mean power.
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A frame carries unit energy, so the SNR in decibels is the frame energy
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over the total noise across all `d` real dimensions, and every scheme in
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a comparison spends the same energy, bandwidth and rate. The
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jamming-to-signal ratio is the jammer energy over the same frame energy.
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Logarithms in an entropy or an information rate are base two.
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## Figure and table map
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| Artifact | Script | Data |
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|---|---|---|
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| Fig. 2 SER against SNR | `exp_full.stage_A` | `sec_snr.csv` |
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| Fig. 3 key length | `exp_full.stage_B` | `sec_keylen.csv` |
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| Fig. 4 jamming (4 schemes) | `exp_full.stage_C`, `stage_L` | `sec_jam_cmp.csv`, `sec_jam.csv` |
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| Fig. 5 key sensitivity | `exp_full.stage_I` | `sec_sens_cmp.csv` |
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| Fig. 6 brute-force search | `exp_full.stage_I`, `stage_F`, `stage_J` | `sec_brute_cmp.csv`, `sec_brute.csv` |
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| Fig. 7 known-plaintext attack | `exp_kpa`, `exp_permkpa` | `kpa.csv`, `pkpa.csv` |
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| Fig. 8 real token streams | `exp_real_sec` | `real_sec_ter.csv` |
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| Scheme comparison table | `exp_full.stage_E` | `sec_compare.csv` |
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| Key family table | `exp_full.stage_D` | `sec_maskfam.csv`, `sec_regjam.csv` |
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| Headline recovery table | `exp_real_sec` | `real_sec_stats.json` |
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| Key refresh tables | `exp_refresh` | `refresh_summary.csv`, `refresh_kpa.csv` |
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| Information-theoretic leakage | `exp_infotheory` | `infotheory.csv` |
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| Semantic similarity | `exp_semantic` | `semantic.csv` |
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| Load and channel-estimate sweeps | `exp_users_csi` | `users.csv`, `csi.csv` |
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| Permutation-variant check | `exp_full.stage_M` | `perm_variant.csv` |
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Run one stage on its own with `python code/exp_full.py stage_B`, or the whole chain with no argument.
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| Closed-form and symbolic checks | `verify_math` | `verify_math.csv` |
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## Security scope
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The analysis covers an adversary that observes transmitted frames. The
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masking is linear, so an adversary that also learns the indices some
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frames carried recovers the key from a few frames, which `exp_kpa.py`
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measures. The key must therefore be refreshed per coherence block from a shared
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seed. `exp_refresh.py` implements that layer and shows why it has to
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draw from the transformations that leave the decision statistic
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invariant: a refresh that installs fresh orthogonal keys instead costs
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the legitimate users a factor of 2.3, while the invariant
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refresh costs nothing and raises the per-block key from 23.8 to 364.6
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bits.
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## License
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MIT for the code. The AG News data and the language-model tokenizer are
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obtained from their own sources under their own terms.
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