KiHoLee
3a9a5eebf4
check_family_enum.py now runs both attacks against both families. The outsider ranks the L-1 Walsh rows; the insider, holding m_v, ranks the L-1 products m_v .* m_r, which works because Walsh rows are closed under the elementwise product and the per-block sign cancels in m_u .* m_v. Both need a list to rank, and only the structured family supplies one: the structured family falls at 0.905 from one frame at 10 dB and 0.990 from four, the refresh takes the outsider to 0.000 and leaves the insider at 0.980, and the learned family gives 0.000 throughout. exp_full.stage_N sweeps the learned family over the same SNR grid at the same frame count as stage_A, so Fig. 2 can carry both families and a reader can see what the key space costs at every SNR rather than at one point. check_consistency.py gains four assertions for the key-space measurements and two for the learned sweep, 82 in all. README: the assertion count was two rounds stale, and the figure map omitted family_enum, cov_attack and maskdegen, whose CSVs back quoted manuscript numbers.
124 lines
6.1 KiB
Markdown
124 lines
6.1 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 diag_maskdegen.py # learned-key support degeneracy
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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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| Key-space attacks (Sec. VI-F) | `check_family_enum` | `family_enum.csv` |
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| Covariance attack (Sec. IV) | `check_cov_attack` | `cov_attack.csv` |
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| Learned-key degeneracy (Sec. VI-F) | `diag_maskdegen` | `maskdegen.csv` |
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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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