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Audit round: fair OMA reference, dense grids, covariance-attack checks

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Resource-match the OMA reference in the key-length sweep (oma_ser_keylen),
which gives it the L/16 combining gain the longer frame allows. The
proposal now passes a resource-matched OMA by 1.27x at L=64 rather than
the 4.3x reported against a fixed-d reference.

Densify the JSR, sensitivity, and brute-force grids so the curves are
smooth, give the index cipher its channel floor instead of error-free
reception, and add the permutation-key known-plaintext attack
(exp_permkpa) so Fig. 7 carries a conventional linear scheme.

Add check_cov_attack.py and check_cov_ceiling.py: a referee raised a
ciphertext-only second-order attack; the exact-population test shows the
received covariance leaks only a sparse rank-deficient subset of the key
Gram and leaves the eavesdropper at the random-guess level.

Dump verify_math.csv, move the superseded V=256 pilot CSVs to data/pilot.
This commit is contained in:
KiHoLee
2026-08-16 23:48:39 +09:00
parent fbbad154b2
commit 3529ab1918
28 changed files with 755 additions and 123 deletions
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data/sec_brute_cmp.csv
+14 -7
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@@ -1,8 +1,15 @@
K,ser_mask,ser_perm,ser_pad,best_kappa,best_frac
1,0.9993678262,0.9999544349,0.9999847412,0.1909643153,0.0160546875
10,0.9948030015,0.9999095052,0.9998474121,0.4626973216,0.041015625
100,0.9825717055,0.9998648567,0.9984741211,0.6342127242,0.0658203125
1000,0.9568330187,0.9998315989,0.9847412109,0.7432459045,0.084296875
10000,0.909109588,0.9997988333,0.8474121094,0.8165387856,0.1025
100000,0.8435049061,0.9997690911,0,0.8680494354,0.1190234375
1000000,0.7503523409,0.9997409661,0,0.905556646,0.1346484375
1,0.9993527855,0.9999542171,0.9999893771,0.1909643153,0.0160546875
3,0.9977393447,0.9999284847,0.9999681312,0.3326517476,0.0281640625
10,0.9952796283,0.9998968587,0.9998937706,0.450762326,0.043046875
30,0.9895360243,0.9998741146,0.9996813118,0.5560672497,0.05375
100,0.9834025595,0.9998495442,0.998937706,0.6290900875,0.0653125
300,0.9728417994,0.9998307015,0.996813118,0.688703621,0.0741796875
1000,0.9568452325,0.9998081233,0.9893770599,0.7427389508,0.0848046875
3000,0.9366731394,0.9997877864,0.9681311798,0.7822538913,0.094375
10000,0.9075372546,0.9997705208,0.8937705994,0.8169040678,0.1025
30000,0.8818766363,0.9997525081,0.6813117981,0.8434367197,0.1109765625
65536,0.8592861449,0.9997413851,0.303815,0.8592030095,0.1162109375
100000,0.8443657504,0.9997355745,0.303815,0.8678447033,0.1189453125
300000,0.802269081,0.9997181429,0.303815,0.8874619916,0.1271484375
1000000,0.7622180175,0.9997021224,0.303815,0.9034448904,0.1346875
1 K ser_mask ser_perm ser_pad best_kappa best_frac
2 1 0.9993678262 0.9993527855 0.9999544349 0.9999542171 0.9999847412 0.9999893771 0.1909643153 0.0160546875
3 10 3 0.9948030015 0.9977393447 0.9999095052 0.9999284847 0.9998474121 0.9999681312 0.4626973216 0.3326517476 0.041015625 0.0281640625
4 100 10 0.9825717055 0.9952796283 0.9998648567 0.9998968587 0.9984741211 0.9998937706 0.6342127242 0.450762326 0.0658203125 0.043046875
5 1000 30 0.9568330187 0.9895360243 0.9998315989 0.9998741146 0.9847412109 0.9996813118 0.7432459045 0.5560672497 0.084296875 0.05375
6 10000 100 0.909109588 0.9834025595 0.9997988333 0.9998495442 0.8474121094 0.998937706 0.8165387856 0.6290900875 0.1025 0.0653125
7 100000 300 0.8435049061 0.9728417994 0.9997690911 0.9998307015 0 0.996813118 0.8680494354 0.688703621 0.1190234375 0.0741796875
8 1000000 1000 0.7503523409 0.9568452325 0.9997409661 0.9998081233 0 0.9893770599 0.905556646 0.7427389508 0.1346484375 0.0848046875
9 3000 0.9366731394 0.9997877864 0.9681311798 0.7822538913 0.094375
10 10000 0.9075372546 0.9997705208 0.8937705994 0.8169040678 0.1025
11 30000 0.8818766363 0.9997525081 0.6813117981 0.8434367197 0.1109765625
12 65536 0.8592861449 0.9997413851 0.303815 0.8592030095 0.1162109375
13 100000 0.8443657504 0.9997355745 0.303815 0.8678447033 0.1189453125
14 300000 0.802269081 0.9997181429 0.303815 0.8874619916 0.1271484375
15 1000000 0.7622180175 0.9997021224 0.303815 0.9034448904 0.1346875