KiHoLee
25b5891b04
- exclude the all-ones Walsh-Hadamard row and test every key family against the all-ones guess - pass the model dimension to the noise scaling so the key-length sweep runs at a fixed per-dimension SNR - known-plaintext attack with nested accumulation and common random numbers, averaged over 40 collections - key sensitivity and brute-force search extended to the permutation key and the index cipher - make_tables regenerates all three result tables from the CSVs
80 lines
2.5 KiB
Python
80 lines
2.5 KiB
Python
"""Generate the LaTeX rows of the three result tables 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"Global-permutation key~\cite{chen2023shuffling}",
|
|
"index_cipher": "Per-user 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 eavesdropper",
|
|
}
|
|
|
|
|
|
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"]))
|
|
for r in rows:
|
|
b = r["scheme"] == "proposed"
|
|
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")):
|
|
cells = [cell(r[k], False) for k in
|
|
("legit_ser", "eve_ser", "eve_ones_ser", "mask_xcorr")]
|
|
print(f"{NAME[r['family']]} & " + " & ".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" \\")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
compare_table(); print()
|
|
maskfam_table(); print()
|
|
real_table()
|