KiHoLee
c31e6a3fe0
Give the orthogonal-access jammer its own Rayleigh channel in oma_ser_jammed, matching the convention every simulated scheme already used. Without it the closed-form curve faced a jammer at full power in every frame while the Monte Carlo curves faced a fading one, which inverted the ordering of the comparison. Measure the outsider error rate for the fixed-key and naive-refresh cases as well, and emit the two refresh tables from make_tables.py, so no cell of the paper is hand-typed.
103 lines
3.6 KiB
Python
103 lines
3.6 KiB
Python
"""Generate the LaTeX rows of the three result tables from the CSVs, so
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that every table in the paper is reproducible from data/ (TIFS mandate).
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Prints the tabular bodies; paste into main.tex without edits.
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"""
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from __future__ import annotations
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import csv
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import json
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import math
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from pathlib import Path
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DATA = Path(__file__).resolve().parents[1] / "data"
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NAME = {
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"proposed": r"\textbf{Proposed keyed masking}",
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"public_mask": "Public masks",
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"perm_key": r"Global-permutation key~\cite{chen2023shuffling}",
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"index_cipher": "Per-user index cipher",
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"oma_plain": "OMA (no encryption)",
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"random": "Random",
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"hadamard": "Walsh-Hadamard",
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"learned": "Learned",
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"learned_reg": r"Regularized~\eqref{eq:regloss}",
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}
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RECEIVER = {
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"legit": "Legitimate", "oma": "OMA",
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"insider": "Insider", "eve": "Outsider",
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}
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def f3(x: str) -> str:
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"""Three decimals, or an em-dash for a value that does not apply."""
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try:
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v = float(x)
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except (TypeError, ValueError):
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return "--"
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return "--" if math.isnan(v) else f"{v:.3f}"
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def cell(x: str, bold: bool) -> str:
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s = f3(x)
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if s == "--":
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return "--"
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return rf"$\mathbf{{{s}}}$" if bold else f"${s}$"
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def compare_table():
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print("% Table: scheme comparison (from sec_compare.csv)")
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rows = list(csv.DictReader(open(DATA / "sec_compare.csv")))
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order = ["public_mask", "perm_key", "index_cipher", "oma_plain", "proposed"]
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rows.sort(key=lambda r: order.index(r["scheme"]))
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for r in rows:
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b = r["scheme"] == "proposed"
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cells = [cell(r[k], b) for k in
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("legit_ser", "eve_out", "eve_in", "jam0_ser")]
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print(f"{NAME[r['scheme']]} & " + " & ".join(cells) + r" \\")
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def maskfam_table():
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print("% Table: key families (from sec_maskfam.csv)")
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for r in csv.DictReader(open(DATA / "sec_maskfam.csv")):
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cells = [cell(r[k], False) for k in
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("legit_ser", "eve_ser", "eve_ones_ser", "mask_xcorr")]
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print(f"{NAME[r['family']]} & " + " & ".join(cells) + r" \\")
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def real_table():
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print("% Table: headline recovery (from real_sec_stats.json)")
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st = json.loads((DATA / "real_sec_stats.json").read_text())
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rec = st["recovery"]
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snrs = sorted(rec, key=float)
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for key in ("legit", "oma", "insider", "eve"):
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cells = " & ".join(f"${rec[s][key]:.3f}$" for s in snrs)
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print(f"{RECEIVER[key]} & {cells}" + r" \\")
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def refresh_tables():
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print("% Table: key refresh (from refresh_summary.csv)")
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for r in csv.DictReader(open(DATA / "refresh_summary.csv")):
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b = r["scheme"] == "Invariant"
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name = r"\textbf{Invariant}" if b else r["scheme"]
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f = (lambda t: r"\mathbf{" + t + "}") if b else (lambda t: t)
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print(f"{name} & ${f(format(float(r['legit']), '.3f'))}$ & "
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f"${f(format(float(r['eve']), '.4f'))}$ & "
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f"${f(format(float(r['entropy_bits']), '.1f'))}$~bits" + r" \\")
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print()
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print("% Table: known plaintext across a refresh (from refresh_kpa.csv)")
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rows = {r["n_frames"]: r for r in csv.DictReader(open(DATA / "refresh_kpa.csv"))}
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keep = ["2", "8", "64"]
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print("Frames used by the attacker & "
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+ " & ".join(f"${k}$" for k in keep) + r" \\")
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for lbl, key in (("Same block", "ser_same_block"),
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("Next block", "ser_next_block")):
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print(f"{lbl} & "
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+ " & ".join(f"${float(rows[k][key]):.3f}$" for k in keep)
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+ r" \\")
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if __name__ == "__main__":
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compare_table(); print()
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maskfam_table(); print()
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real_table(); print()
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refresh_tables()
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