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Ship the learned-key pipeline, without which six figures cannot be rebuilt

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The package was missing every script behind the KM (lrn.) curves and
both learned table rows: exp_learned's driver, the merge that folds the
learned rows into sec_compare.csv and refresh_summary.csv, and the
report that reads the learned numbers back. It was also missing
sec_keylen_perm.csv, so Fig. 3 could not be regenerated at all, and the
two diagnostics that answer why a fixed key beats a learned one here and
where a learned mask would win instead.

The learned artifacts themselves are regenerated. They were trained on
the cross-entropy alone, which drifts to disjoint sparse supports: 99
percent of each key's energy on about six of the 64 entries, so a digit
is decided over a sixth of its period and the key set is a choice of
support rather than a dense direction in R^L. They are now the
regularized keys of Section V-C, and check_consistency asserts which of
the two families the figures draw.

Verified from inside this repository: replot_security.py rebuilds all
seven result figures, make_tables.py reproduces both result tables, and
check_consistency.py passes every check that does not need the
manuscript.

The README now lists what ships. Its run list, layout and figure map had
none of the learned pipeline, named two tables the manuscript renders as
prose, and gave Fig. 3 no data file for its permutation curve.
This commit is contained in:
KiHoLee
2026-08-28 23:58:01 +09:00
parent ffe56b4b25
commit 138897aa8d
35 changed files with 853 additions and 168 deletions
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code/check_consistency.py
+34 -12
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@@ -55,8 +55,11 @@ chk("24 and 35 in tex", "$24$ to\n$35$~percent" in tex or "$24$ to $35$~percent"
ew = [float(x["eve_wrong"]) for x in sn]
ch = float(sn[0]["chance"])
dev = max(abs(x - ch) for x in ew)
chk("outsider at chance to 3.5e-4", dev < 3.6e-4, "max deviation %.2e" % dev)
chk("3.5e-4 in tex", "$3.5\\times10^{-4}$" in tex, "searched tex",
_ewl = [float(x["eve_wrong"]) for x in rows("sec_snr_learned.csv")]
dev = max(dev, max(abs(x - ch) for x in _ewl))
chk("outsider at chance to 4e-4, both families", dev < 4.0e-4,
"max deviation %.2e" % dev)
chk("4e-4 in tex", "$4\\times10^{-4}$" in tex, "searched tex",
needs_tex=True)
# the main configuration's legitimate rate, the reference every later
@@ -212,8 +215,8 @@ chk("learned support overlap 0.10",
round(float(md["learned"]["mean_overlap"]), 2) == 0.10,
md["learned"]["mean_overlap"])
chk("degeneracy numbers in tex",
"$5$ to $8$ of the $64$ entries" in tex
and "overlapping by $0.10$" in " ".join(tex.split()),
"$5$ to $8$ of the $64$ entries" in " ".join(tex.split())
and "only $0.10$ of the smaller of any two such sets" in " ".join(tex.split()),
"searched tex", needs_tex=True)
# --- why the permutation key is granted a shared permutation ---------
@@ -224,7 +227,7 @@ chk("per-user permutation legitimate rate",
abs(pv["per_user"] - 0.129) < 1e-3, "%.5f" % pv["per_user"])
if HAVE_TEX:
chk("quoted permutation cost in tex",
"from $0.053$ to $0.129$" in " ".join(tex.split()),
"at $0.129$ against $0.053$" in " ".join(tex.split()),
"searched tex", needs_tex=True)
@@ -299,22 +302,40 @@ chk("learned family tracks the structured one over the SNR range",
for r in _sl),
max(float(r["legit"]) / _sn[float(r["snr_db"])]
for r in _sl)))
chk("learned 0.064 at 10 dB",
abs([float(r["legit"]) for r in _sl
if float(r["snr_db"]) == 10.0][0] - 0.064) < 5e-4,
"%.5f" % [float(r["legit"]) for r in _sl
if float(r["snr_db"]) == 10.0][0])
_l10 = [float(r["legit"]) for r in _sl if float(r["snr_db"]) == 10.0][0]
chk("learned 0.061 at 10 dB", abs(_l10 - 0.061) < 5e-4, "%.5f" % _l10)
# The learned curves must be the regularized keys, not the unpenalized
# ones. Both are measured in sec_maskfam.csv and they differ by 0.003,
# which is larger than the spread of either, so matching the right row
# pins which family every figure draws. Cross-entropy alone drifts to a
# slot allocation whose key space is a support rather than a sphere, so
# drawing it would not support the key-space claim.
_fam = {r["family"]: float(r["legit_ser"]) for r in rows("sec_maskfam.csv")}
chk("the plotted learned family is the regularized one",
abs(_l10 - _fam["learned_reg"]) < abs(_l10 - _fam["learned"])
and abs(_l10 - _fam["learned_reg"]) < 1.5e-3,
"plotted %.5f, reg %.5f, plain %.5f"
% (_l10, _fam["learned_reg"], _fam["learned"]))
_bl = {int(r["K"]): float(r["ser_mask"])
for r in rows("sec_brute_learned.csv")}
chk("learned key resists a million random guesses",
abs(_bl[1_000_000] - 0.72) < 5e-3, "%.4f" % _bl[1_000_000])
abs(_bl[1_000_000] - 0.71) < 5e-3, "%.4f" % _bl[1_000_000])
_kl = {(float(r["snr_db"]), int(r["n_frames"])): float(r["eve_ser"])
for r in rows("kpa_learned.csv")}
chk("learned key falls to known plaintext like the structured one",
_kl[(10.0, 4)] < 0.08 and _kl[(10.0, 1)] > 0.9,
"N=1 %.3f, N=4 %.4f" % (_kl[(10.0, 1)], _kl[(10.0, 4)]))
_rs = {float(r["snr_db"]): float(r["ter_legit"])
for r in rows("real_sec_ter.csv")}
_rl = {float(r["snr_db"]): float(r["ter_legit"])
for r in rows("real_sec_ter_learned.csv")}
_rat = [_rl[k] / _rs[k] for k in _rs]
chk("learned keeps its uniform-source distance on real text",
all(1.10 < v < 1.25 for v in _rat),
"ratio %.2f to %.2f" % (min(_rat), max(_rat)))
# --- trends, which the value assertions above cannot see ---------------
_snr = rows("sec_snr.csv")
_lg = [float(r["legit"]) for r in _snr]
@@ -387,7 +408,8 @@ for _k, _c in [("V8 cross-period remainder", 0.0005),
and float(_vm[_k]["abs_err"]) <= _c,
_vm[_k]["empirical"] if _k in _vm else "row missing")
chk("format-matched OMA quoted as 0.055",
"$0.055$ at $10$~dB against the proposed" in tex, "Section VI-B",
"$0.055$ at $10$~dB" in tex and "the proposed $0.053$" in tex,
"Section VI-B",
needs_tex=True)
# --- tables against their generator -----------------------------------