KiHoLee
58a85c30f6
The jammed OMA model now matches the transmit chain the paper describes, the key-length sweep averages the eavesdropper over eight substitute-key draws, and verify_math gains the coded-OMA outage reference, symbolic checks of the three algebraic identities, and the cross-period remainder of Proposition 2. The README records the energy and channel conventions.
112 lines
5.3 KiB
Markdown
112 lines
5.3 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-V5, 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 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_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_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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| 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 nearly three, while the invariant
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refresh costs nothing and raises the per-block key from 15.0 to 64.8
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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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