KiHoLee
3cf3b6c3a7
stage_F was missed in the structured-family regeneration, so sec_brute.csv and sec_sens.csv still carried learned-family results. Re-running it moves the L=16 brute-force point from 0.7495 to 0.5916, which now agrees with sec_brute_cmp.csv rather than contradicting it. make_tables.py bolds the Walsh-Hadamard row the way it already bolds the proposed and invariant rows, since that family is the main configuration.
118 lines
4.3 KiB
Python
118 lines
4.3 KiB
Python
"""Generate the LaTeX rows of every result table from the CSVs, so
|
|
that every table in the paper is reproducible from data/ (TIFS mandate).
|
|
Prints the tabular bodies; paste into main.tex without edits.
|
|
"""
|
|
from __future__ import annotations
|
|
import csv
|
|
import json
|
|
import math
|
|
from pathlib import Path
|
|
|
|
DATA = Path(__file__).resolve().parents[1] / "data"
|
|
|
|
NAME = {
|
|
"proposed": r"\textbf{Proposed keyed masking}",
|
|
"public_mask": "Public masks",
|
|
"perm_key": r"Permutation key~\cite{chen2023shuffling}",
|
|
"index_cipher": "Index cipher",
|
|
"oma_plain": "OMA (no encryption)",
|
|
"random": "Random",
|
|
"hadamard": "Walsh-Hadamard",
|
|
"learned": "Learned",
|
|
"learned_reg": r"Regularized~\eqref{eq:regloss}",
|
|
}
|
|
RECEIVER = {
|
|
"legit": "Legitimate", "oma": "OMA",
|
|
"insider": "Insider", "eve": "Outsider",
|
|
}
|
|
|
|
|
|
def f3(x: str) -> str:
|
|
"""Three decimals, or an em-dash for a value that does not apply."""
|
|
try:
|
|
v = float(x)
|
|
except (TypeError, ValueError):
|
|
return "--"
|
|
return "--" if math.isnan(v) else f"{v:.3f}"
|
|
|
|
|
|
def cell(x: str, bold: bool) -> str:
|
|
s = f3(x)
|
|
if s == "--":
|
|
return "--"
|
|
return rf"$\mathbf{{{s}}}$" if bold else f"${s}$"
|
|
|
|
|
|
def compare_table():
|
|
print("% Table: scheme comparison (from sec_compare.csv)")
|
|
rows = list(csv.DictReader(open(DATA / "sec_compare.csv")))
|
|
order = ["public_mask", "perm_key", "index_cipher", "oma_plain", "proposed"]
|
|
rows.sort(key=lambda r: order.index(r["scheme"]))
|
|
# stage_E does not jam the orthogonal reference, because the jammer an
|
|
# OMA user faces is targeted at public slots rather than mask-matched
|
|
# or mask-blind. stage_L measures that case, so the cell comes from
|
|
# there instead of being left empty.
|
|
jam = {float(r["jsr_db"]): r
|
|
for r in csv.DictReader(open(DATA / "sec_jam_cmp.csv"))}
|
|
oma_jam = jam[0.0]["oma_targeted"]
|
|
for r in rows:
|
|
b = r["scheme"] == "proposed"
|
|
if r["scheme"] == "oma_plain" and f3(r["jam0_ser"]) == "--":
|
|
r["jam0_ser"] = oma_jam
|
|
cells = [cell(r[k], b) for k in
|
|
("legit_ser", "eve_out", "eve_in", "jam0_ser")]
|
|
print(f"{NAME[r['scheme']]} & " + " & ".join(cells) + r" \\")
|
|
|
|
|
|
def maskfam_table():
|
|
print("% Table: key families (from sec_maskfam.csv)")
|
|
for r in csv.DictReader(open(DATA / "sec_maskfam.csv")):
|
|
# the structured family is the main configuration, so its row is
|
|
# emphasized the same way the proposed row is in the comparison
|
|
b = r["family"] == "hadamard"
|
|
cells = [cell(r[k], b) for k in
|
|
("legit_ser", "eve_ser", "eve_ones_ser", "mask_xcorr")]
|
|
name = NAME[r["family"]]
|
|
if b:
|
|
name = r"\textbf{" + name + "}"
|
|
print(f"{name} & " + " & ".join(cells) + r" \\")
|
|
|
|
|
|
def real_table():
|
|
print("% Table: headline recovery (from real_sec_stats.json)")
|
|
st = json.loads((DATA / "real_sec_stats.json").read_text())
|
|
rec = st["recovery"]
|
|
snrs = sorted(rec, key=float)
|
|
for key in ("legit", "oma", "insider", "eve"):
|
|
cells = " & ".join(f"${rec[s][key]:.3f}$" for s in snrs)
|
|
print(f"{RECEIVER[key]} & {cells}" + r" \\")
|
|
|
|
|
|
def refresh_tables():
|
|
print("% Table: key refresh (from refresh_summary.csv)")
|
|
for r in csv.DictReader(open(DATA / "refresh_summary.csv")):
|
|
b = r["scheme"] == "Invariant"
|
|
name = r"\textbf{Invariant}" if b else r["scheme"]
|
|
f = (lambda t: r"\mathbf{" + t + "}") if b else (lambda t: t)
|
|
print(f"{name} & ${f(format(float(r['legit']), '.3f'))}$ & "
|
|
f"${f(format(float(r['eve']), '.4f'))}$ & "
|
|
f"${f(format(float(r['entropy_bits']), '.1f'))}$~bits" + r" \\")
|
|
print()
|
|
print("% Table: known plaintext across a refresh (from refresh_kpa.csv)")
|
|
rows = {r["n_frames"]: r for r in csv.DictReader(open(DATA / "refresh_kpa.csv"))}
|
|
keep = ["2", "8", "64"]
|
|
print("Frames used by the attacker & "
|
|
+ " & ".join(f"${k}$" for k in keep) + r" \\")
|
|
for lbl, key in (("Same block", "ser_same_block"),
|
|
("Next block", "ser_next_block")):
|
|
print(f"{lbl} & "
|
|
+ " & ".join(f"${float(rows[k][key]):.3f}$" for k in keep)
|
|
+ r" \\")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
compare_table(); print()
|
|
maskfam_table(); print()
|
|
real_table(); print()
|
|
refresh_tables()
|