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 and L: SNR sweep, key length, jamming across
                        schemes, key families, scheme comparison, attack difficulty
  exp_kpa.py            stage H: known-plaintext attack on the key
  exp_refresh.py        stage K: the key-refresh layer, invariance group
  exp_permkpa.py        permutation-key known-plaintext attack (Fig. 7)
  check_cov_*.py        ciphertext-only covariance-attack checks (referee M1)
  exp_real_sec.py       stage G: real BERT WordPiece token streams
  verify_math.py        closed-form checks V1-V5, PASS/FAIL and verify_math.csv
  replot_security.py    every result figure, from data/ to fig/
  make_tables.py        LaTeX rows of every result table, 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.

python verify_math.py        # closed-form verification, prints PASS/FAIL
python exp_full.py           # stages A-F and L
python exp_kpa.py            # known-plaintext attack
python exp_refresh.py        # the key-refresh layer
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, key recovery 4242, brute-force search 31, cross-scheme comparison 11, key refresh 5150. 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 (4 schemes)	exp_full.stage_L	sec_jam_cmp.csv, sec_jam.csv
Fig. 5 key sensitivity	exp_full.stage_I	sec_sens_cmp.csv
Fig. 6 brute-force search	exp_full.stage_J	sec_brute_cmp.csv, sec_brute.csv
Fig. 7 known-plaintext attack	exp_kpa, exp_permkpa	kpa.csv, pkpa.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
Key refresh tables	exp_refresh	refresh_summary.csv, refresh_kpa.csv

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. exp_refresh.py implements that layer and shows why it has to draw from the transformations that leave the decision statistic invariant: a refresh that installs fresh orthogonal keys instead costs the legitimate users a factor of nearly three, while the invariant refresh costs nothing and raises the per-block key from 15.0 to 64.8 bits.

License

MIT for the code. The AG News data and the language-model tokenizer are obtained from their own sources under their own terms.