Final revision: audit fixes, cross-scheme comparisons, known-plaintext stage

- 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

code/make_tables.py

@@ -1,9 +1,11 @@
-"""Generate the LaTeX rows of the two result tables from the CSVs, so
+"""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 body; paste into main.tex without edits.
+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"
@@ -17,39 +19,61 @@ NAME = {
     "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:
-        return f"{float(x):.3f}"
-    except ValueError:
+        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 = sorted(rows, key=lambda r: order.index(r["scheme"]))
+    rows.sort(key=lambda r: order.index(r["scheme"]))
     for r in rows:
-        cells = [f3(r["legit_ser"]), f3(r["eve_out"]), f3(r["eve_in"]),
-                 f3(r["jam0_ser"])]
-        if r["scheme"] == "proposed":
-            cells = [rf"$\mathbf{{{c}}}$" for c in cells]
-        else:
-            cells = [f"${c}$" if c != "--" else "--" for c in cells]
+        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")):
-        print(f"{NAME[r['family']]} & ${f3(r['legit_ser'])}$ & "
-              f"${f3(r['eve_ser'])}$ & ${f3(r['mask_xcorr'])}$" + r" \\")
+        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()
+    compare_table(); print()
+    maskfam_table(); print()
+    real_table()