Main configuration d=256, L=64: all data, figures and checks re-run

Every OMA reference takes the L/16 combining gain so the comparison
stays resource matched, four hardcoded copies of the configuration are
replaced by MAIN_D or the main curve, and stage_J's K-by-L Gaussian
draw becomes its exact scalar Beta equivalent.

code/check_consistency.py

@@ -47,15 +47,24 @@ lg = [float(x["legit"]) for x in sn]
 om = [float(x["oma"]) for x in sn]
 rel = [(a - b) / b * 100 for a, b in zip(lg, om)]
 chk("legit below OMA at every SNR", max(rel) < 0, "max relative %+.2f%%" % max(rel))
-chk("gain 1.3 to 7.9 percent",
+chk("gain 24 to 35 percent",
-    round(-max(rel), 1) == 1.3 and round(-min(rel), 1) == 7.9,
+    round(-max(rel)) == 24 and round(-min(rel)) == 35,
     "%.2f to %.2f percent" % (-max(rel), -min(rel)))
-chk("1.3 and 7.9 in tex", "$1.3$ to\n$7.9$~percent" in tex or "$1.3$ to $7.9$~percent" in tex,
+chk("24 and 35 in tex", "$24$ to\n$35$~percent" in tex or "$24$ to $35$~percent" in tex,
     "searched tex", needs_tex=True)
 ew = [float(x["eve_wrong"]) for x in sn]
 ch = float(sn[0]["chance"])
-chk("outsider at chance to 2e-5", max(abs(x - ch) for x in ew) < 2e-5,
+dev = max(abs(x - ch) for x in ew)
-    "max deviation %.2e" % 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",
+    needs_tex=True)
+
+# the main configuration's legitimate rate, the reference every later
+# assertion compares against; taken from the curve the main
+# configuration produced rather than looked up by key length
+MAIN_LEGIT = [float(x["legit"]) for x in sn if float(x["snr_db"]) == 10][0]
+chk("main legitimate 0.053", round(MAIN_LEGIT, 3) == 0.053,
+    "%.4f" % MAIN_LEGIT)
 
 # --- Fig. 3: key-length ratio ----------------------------------------
 k = rows("sec_keylen.csv")
@@ -70,11 +79,10 @@ chk("keys exactly orthogonal in the sweep",
 
 # --- Fig. 4: jamming --------------------------------------------------
 g = col("sec_jam_gap.csv", "gap_db")
-chk("gap 5.5-6.3 dB", round(min(g), 1) == 5.5 and round(max(g), 1) == 6.3,
+chk("gap 10.1-11.1 dB", round(min(g), 1) == 10.1 and round(max(g), 1) == 11.1,
     "%.3f to %.3f" % (min(g), max(g)))
 lin = (10 ** (min(g) / 10), 10 ** (max(g) / 10))
-chk("about four times power", lin[0] < 4.5 and lin[1] > 3.4,
+chk("more than ten times power", lin[0] > 10.0, "%.2f to %.2f" % lin)
-    "%.2f to %.2f" % lin)
 j = rows("sec_jam_cmp.csv")
 dmax = max(abs(float(r["blind"]) - float(r["perm_blind"])) for r in j)
 chk("within 0.002", dmax <= 0.002, "%.5f" % dmax)
@@ -83,32 +91,40 @@ chk("no stale 8.1 dB", "$8.1$~dB" not in tex, "searched tex", needs_tex=True)
 # --- Fig. 6: brute force ---------------------------------------------
 b = rows("sec_brute_cmp.csv")
 sm = float(b[-1]["ser_mask"])
-chk("brute 0.59 at 1e6", round(sm, 2) == 0.59, "%.4f" % sm)
+chk("brute 0.67 at 1e6", round(sm, 2) == 0.67, "%.4f" % sm)
-chk("0.59 in tex", "$0.59$" in tex, "searched tex", needs_tex=True)
+chk("0.67 in tex", "$0.67$" in tex, "searched tex", needs_tex=True)
-pad0 = next((x["K"] for x in b if float(x["ser_pad"]) < 0.27), None)
+closed = (ch - sm) / (ch - MAIN_LEGIT)
+chk("brute closes about a third", 0.30 < closed < 0.40, "%.3f" % closed)
+bf = float(b[-1]["best_frac"]) * 100
+chk("permutation 3.4 percent of positions", round(bf, 1) == 3.4, "%.2f" % bf)
+pad0 = next((x["K"] for x in b if float(x["ser_pad"]) < 0.1), None)
 chk("index cipher collapses at 65536", pad0 == "65536", str(pad0))
 
 # --- Fig. 7: known plaintext -----------------------------------------
 kp = rows("kpa.csv")
-legit = float([x for x in k if int(x["L"]) == 16][0]["legit_ser"])
+legit = MAIN_LEGIT
 thr = legit * 1.02
 first20 = next((x["n_frames"] for x in kp
                 if int(x["snr_db"]) == 20 and float(x["eve_ser"]) <= thr), None)
 first10 = next((x["n_frames"] for x in kp
                 if int(x["snr_db"]) == 10 and float(x["eve_ser"]) <= thr), None)
-chk("KPA five frames at 20 dB", first20 == "5", "first N = %s" % first20)
+chk("KPA three frames at 20 dB", first20 == "3", "first N = %s" % first20)
-chk("KPA twenty-four frames at 10 dB", first10 == "24", "first N = %s" % first10)
+chk("KPA ten frames at 10 dB", first10 == "10", "first N = %s" % first10)
+kp0 = [x for x in kp if int(x["snr_db"]) == 0]
+w0 = float(kp0[-1]["eve_ser"]) / legit
+chk("0 dB no longer holds", w0 < 1.03, "64 frames reach %.3f of legitimate" % w0)
 pk = rows("pkpa.csv")
 p6 = float([x for x in pk if x["n_frames"] == "6"][0]["eve_ser"])
-chk("perm KPA at N=6 near its own 0.258", abs(p6 - 0.258) < 0.005, "%.4f" % p6)
+chk("perm KPA at N=6 near its own legitimate",
+    abs(p6 - MAIN_LEGIT) < 0.005, "%.4f" % p6)
 
 # --- refresh ----------------------------------------------------------
 rs = {x["scheme"]: x for x in rows("refresh_summary.csv")}
-chk("refresh 64.8 bits",
+chk("refresh 364.6 bits",
-    round(float(rs["Invariant"]["entropy_bits"]), 1) == 64.8,
+    round(float(rs["Invariant"]["entropy_bits"]), 1) == 364.6,
     "%.3f" % float(rs["Invariant"]["entropy_bits"]))
-chk("fixed key 15.0 bits",
+chk("fixed key 23.8 bits",
-    round(float(rs["None (fixed key)"]["entropy_bits"]), 1) == 15.0,
+    round(float(rs["None (fixed key)"]["entropy_bits"]), 1) == 23.8,
     "%.4f" % float(rs["None (fixed key)"]["entropy_bits"]))
 chk("invariant refresh free",
     abs(float(rs["Invariant"]["legit"]) - float(rs["None (fixed key)"]["legit"]))
@@ -119,8 +135,8 @@ chk("invariant refresh free",
 import json
 st = json.loads((base / "data" / "real_sec_stats.json").read_text())
 rec = st["recovery"]["28"]
-chk("headline recovery 78 vs 76 percent",
+chk("headline recovery 96 vs 93 percent",
-    round(rec["legit"] * 100) == 78 and round(rec["oma"] * 100) == 76,
+    round(rec["legit"] * 100) == 96 and round(rec["oma"] * 100) == 93,
     "%.1f vs %.1f" % (rec["legit"] * 100, rec["oma"] * 100))
 chk("legit leads OMA at every point",
     all(st["recovery"][s]["legit"] > st["recovery"][s]["oma"]
@@ -137,19 +153,23 @@ chk("L=8 crowding, proposal behind OMA",
 chk("0.949 and 0.685 in tex", "0.949" in tex and "0.685" in tex,
     "searched tex", needs_tex=True)
 
-# the Fig. 2 inset plots this ratio, so its stated span must hold
+# the OMA-to-proposed ratio the narration quotes
 sr = rows("sec_snr.csv")
 rt = [float(r["oma"]) / float(r["legit"]) for r in sr]
-chk("inset ratio spans 1.01 to 1.09", 1.005 < min(rt) and max(rt) < 1.095,
+chk("ratio spans 1.32 to 1.54", round(min(rt), 2) == 1.32
-    "%.3f to %.3f" % (min(rt), max(rt)))
+    and round(max(rt), 2) == 1.54, "%.3f to %.3f" % (min(rt), max(rt)))
 
 # the three secrets named in the setup
-chk("secret sizes UL=64, perm 64, pad 16",
+chk("secret sizes UL=256, perm 256, pad 16",
-    all(t in tex for t in ["$UL=64$ key entries",
+    all(t in tex for t in ["$UL=256$ key entries",
-                           "one permutation of $64$ positions",
+                           "one permutation of $256$ positions",
                            "$16$ pad\nbits per user"]),
     "searched tex", needs_tex=True)
 
+chk("no stale d=64 configuration in tex",
+    "$d=64$ real dimensions" not in tex and "$d/U=16$" not in tex,
+    "searched tex", needs_tex=True)
+
 # Fig. 5 shows the permutation curve tracking the mask curve
 sc = rows("sec_sens_cmp.csv")
 dv = max(abs(float(r["ser_mask"]) - float(r["ser_perm"])) for r in sc)

code/diag_interference.py

@@ -1,12 +1,12 @@
 # -*- coding: utf-8 -*-
 """Where does the legitimate advantage over OMA go?
 
-An ideal M-ary receiver at the main configuration should reach 0.199 at
+The legitimate curve sits above the single-user M-ary bound, and this
-10 dB against the 0.275 of resource-matched OMA, a factor of 1.38, while
+script splits the distance into its two possible causes: residual
-the system measures 0.257, a factor of 1.07. This script splits the
+multi-user interference, which orthogonal keys need not remove because
-shortfall into its two causes: residual multi-user interference, which
+masking is elementwise, and the distance the trained unit codebook falls
-orthogonal keys do not remove because masking is elementwise, and the
+short of an orthogonal set. Run it against whichever configuration
-distance the trained unit codebook falls short of an orthogonal set.
+exp_full.MAIN_D currently names.
 """
 import math
 import sys
@@ -17,7 +17,7 @@ import torch
 sys.path.insert(0, str(Path(__file__).resolve().parent))
 import sse_lib as L
 from sse_lib import DEVICE, snr_to_sigma2, rayleigh_gain
-from exp_full import main_model
+from exp_full import main_model, oma_ser_keylen
 
 SNR_DB = 10.0
 FRAMES = 400_000
@@ -80,8 +80,7 @@ def main():
     print("user-0 SER, all four users transmitting   : %.4f" % four)
     print("user-0 SER, other users silent            : %.4f" % solo)
     print("OMA, resource matched (closed form)       : %.4f"
-          % L.oma_ser([SNR_DB])[0])
+          % oma_ser_keylen(m.L, SNR_DB))
-    print("ideal 16-ary orthogonal (separate MC)     : 0.1986")
 
 
 if __name__ == "__main__":

code/diag_maskdegen.py

@@ -0,0 +1,63 @@
+# -*- coding: utf-8 -*-
+"""Does unconstrained key training still degenerate at the main configuration?
+
+The manuscript justifies fixing the keys by a measured failure: with the
+keys free, training drives them to disjoint sparse supports, which is an
+orthogonal slot allocation rather than a superposition, and which shrinks
+the key space to the choice of a support. That was measured at d=64 and
+has to be re-measured whenever the configuration moves, because it is
+the reason the structured family is the main one.
+
+Reported per user key: the number of entries holding 99 percent of the
+energy, and the pairwise overlap of those supports. A dense key spreads
+its energy over most of the L entries and the supports coincide; a
+degenerate one concentrates on a few and the supports are disjoint.
+"""
+import sys
+from pathlib import Path
+
+import torch
+
+sys.path.insert(0, str(Path(__file__).resolve().parent))
+from exp_full import get_model, main_model, MAIN_D
+
+
+def support99(w):
+    """Smallest set of entries carrying 99 percent of the key energy."""
+    e = w.pow(2)
+    order = torch.argsort(e, descending=True)
+    c = torch.cumsum(e[order], 0) / e.sum()
+    k = int((c < 0.99).sum()) + 1
+    return set(order[:k].tolist()), k
+
+
+def describe(name, W):
+    L = W.shape[1]
+    sups, ks = [], []
+    for u in range(W.shape[0]):
+        sup, k = support99(W[u])
+        sups.append(sup)
+        ks.append(k)
+    ov = []
+    for i in range(len(sups)):
+        for j in range(i + 1, len(sups)):
+            ov.append(len(sups[i] & sups[j]) / max(1, min(len(sups[i]),
+                                                          len(sups[j]))))
+    print("%-14s L=%3d  99%%-energy entries per key: %s  "
+          "mean pairwise support overlap %.2f"
+          % (name, L, ks, sum(ov) / len(ov)))
+
+
+def main():
+    print("main configuration d=%d" % MAIN_D)
+    m_free = get_model(iters=4000)            # keys learned, nothing frozen
+    describe("learned", m_free.masks().detach().cpu())
+    m_fix = main_model()
+    describe("Walsh-Hadamard", m_fix.masks().detach().cpu())
+    print()
+    print("A degenerate key set shows few entries per key and near-zero")
+    print("overlap; a dense one shows most entries and overlap near one.")
+
+
+if __name__ == "__main__":
+    main()

code/diag_orthobook.py

@@ -1,11 +1,11 @@
 # -*- coding: utf-8 -*-
 """Does an orthogonal unit codebook recover the shortfall?
 
-diag_interference shows the gap to the ideal M-ary receiver is not
+diag_interference shows the gap to the single-user M-ary bound is not
 multi-user interference but the geometry of the trained unit codebook,
-whose Gram matrix carries a root-mean-square off-diagonal of 0.45 where
+whose Gram matrix carries a large root-mean-square off-diagonal where an
-an orthogonal set would carry zero. Vu = L = 16 admits an exactly
+orthogonal set would carry zero. Vu <= L admits an exactly orthogonal
-orthogonal set, so this measures what installing one buys.
+set, so this measures what installing one buys.
 
 Two orthogonal sets are tried, because the choice is not free. The
 Walsh-Hadamard set collides with the keys: the rows are closed under the
@@ -22,7 +22,7 @@ import torch
 sys.path.insert(0, str(Path(__file__).resolve().parent))
 import sse_lib as L
 from sse_lib import DEVICE, SSE
-from exp_full import hadamard, base_keys
+from exp_full import hadamard, base_keys, oma_ser_keylen, MAIN_D
 from diag_interference import ser
 
 SNR = [0.0, 10.0, 20.0]
@@ -39,13 +39,13 @@ def fixed_model(B, P=4, vu=16, d=64, U=4):
     return m
 
 
-def hadamard_book(vu=16, Lp=16):
+def hadamard_book(vu=16, Lp=MAIN_D // 4):
     B = torch.zeros(vu, Lp)
     B[:, :vu] = torch.tensor(hadamard(vu).copy(), dtype=torch.float32)
     return B
 
 
-def random_ortho_book(vu=16, Lp=16, seed=7):
+def random_ortho_book(vu=16, Lp=MAIN_D // 4, seed=7):
     g = torch.Generator().manual_seed(seed)
     A = torch.randn(Lp, Lp, generator=g)
     Q, _ = torch.linalg.qr(A)
@@ -68,13 +68,14 @@ def main():
           % ("unit codebook", "max|off|", "0 dB", "10 dB", "20 dB"))
     report("Walsh-Hadamard", fixed_model(hadamard_book()))
     report("random orthogonal", fixed_model(random_ortho_book()))
-    print("%-22s %-10s %-9s %-9s %-9s"
+    from exp_full import main_model
-          % ("trained (paper)", "0.887", "0.8822", "0.2576", "0.0307"))
+    report("trained", main_model())
+    Lp = MAIN_D // 4
     print("%-22s %-10s %-9s %-9s %-9s"
           % ("OMA, resource matched", "-",
-             "%.4f" % L.oma_ser([0.0])[0],
+             "%.4f" % oma_ser_keylen(Lp, 0.0),
-             "%.4f" % L.oma_ser([10.0])[0],
+             "%.4f" % oma_ser_keylen(Lp, 10.0),
-             "%.4f" % L.oma_ser([20.0])[0]))
+             "%.4f" % oma_ser_keylen(Lp, 20.0)))
 
 
 
@@ -97,7 +98,7 @@ def solo_check():
         print("%-22s %-12.4f %-12.4f"
               % (name, ser(m, 10.0, FRAMES, solo=True),
                  ser(m, 10.0, FRAMES, solo=False)))
-    print("single-user ideal M-ary bound (separate MC): 0.1986")
+    print("(solo isolates the candidate set from the superposition)")
 
 
 if __name__ == "__main__":

code/exp_full.py

@@ -2,7 +2,7 @@
 
 Reuses the SSE transmit/receive core from sse_lib.py and adds an
 eavesdropper receiver, a jammer channel, and structured mask families.
-Main configuration d=64, P=4, Vu=16 (V=Vu^P=65,536), U=4 users, matching
+Main configuration d=256, P=4, Vu=16 (V=Vu^P=65,536), U=4 users, matching
 the language-model token vocabulary scale.
 
 Stages (each writes a CSV to ../data; figures come from replot_security.py
@@ -136,7 +136,11 @@ def eve_wrong_mask(U, Lp, seed):
     return W / W.norm(dim=1, keepdim=True) * math.sqrt(Lp)
 
 
-def get_model(P=4, vu=16, d=64, U=4, iters=4000, seed=1, freeze_W=None, tag=""):
+MAIN_D = 256      # embedding dimension of the main configuration
+
+
+def get_model(P=4, vu=16, d=MAIN_D, U=4, iters=4000, seed=1,
+              freeze_W=None, tag=""):
     """Train an SSE model, optionally with fixed (frozen) masks."""
     set_seed(seed)
     m = SSE(P=P, vu=vu, d=d, users=U).to(DEVICE)
@@ -169,7 +173,7 @@ def base_keys(U: int, Lp: int) -> torch.Tensor:
     return torch.tensor(H[1:U + 1, :Lp].copy(), dtype=torch.float32)
 
 
-def main_model(iters=4000, P=4, vu=16, d=64, U=4):
+def main_model(iters=4000, P=4, vu=16, d=MAIN_D, U=4):
     """The main configuration used by every stage below.
 
     The keys are frozen to the structured Walsh-Hadamard family rather
@@ -195,7 +199,7 @@ def stage_A():
     # conventional public-mask scheme: the eavesdropper holds the same
     # (public) masks and decodes exactly like a legitimate user
     eve_p = eval_ser_eve(m, m.masks().detach().cpu(), snr, frames=frames)
-    oma = oma_ser(snr, bits=int(math.log2(m.V)))
+    oma = [oma_ser_keylen(m.L, s, bits=int(math.log2(m.V))) for s in snr]
     chance = 1.0 - (1.0 / m.vu) ** m.P
     write_csv(DATA / "sec_snr.csv",
               ["snr_db", "legit", "eve_wrong", "eve_none", "eve_public",
@@ -236,7 +240,7 @@ def train_sse_reg(m: SSE, iters=4000, batch=256, lr=3e-3, seed=1,
     return m
 
 
-def get_model_reg(P=4, vu=16, d=64, U=4, iters=4000, seed=1):
+def get_model_reg(P=4, vu=16, d=MAIN_D, U=4, iters=4000, seed=1):
     set_seed(seed)
     m = SSE(P=P, vu=vu, d=d, users=U).to(DEVICE)
     train_sse_reg(m, iters=iters, seed=seed)
@@ -314,7 +318,7 @@ def stage_C():
 
 def stage_D():
     print("[D] mask families ...")
-    P, vu, d, U = 4, 16, 64, 4
+    P, vu, d, U = 4, 16, MAIN_D, 4
     Lp = d // P
     fams = {}
     # random fixed masks
@@ -475,8 +479,7 @@ def stage_E():
     # is public so the matched jammer remains buildable
     rows.append(("index_cipher", lg, chance, chance, jm2))
     # S5 OMA digital, no encryption: open to everyone
-    from sse_lib import oma_ser
+    lg5 = oma_ser_keylen(m.L, 10.0, bits=int(math.log2(m.V)))
-    lg5 = oma_ser([10.0], bits=int(math.log2(m.V)))[0]
     rows.append(("oma_plain", lg5, lg5, lg5, float("nan")))
 
     write_csv(DATA / "sec_compare.csv",
@@ -688,7 +691,7 @@ def stage_J():
     mask_arr = np.array([float(r["ser_mask"]) for r in cmp_rows])
     perm_arr = np.array([float(r["ser_perm"]) for r in cmp_rows])
 
-    d, L, V = 64, 16, 65536
+    d, L, V = MAIN_D, MAIN_D // 4, 65536
     # channel floor of the cipher receiver, read from the stage-I curve
     # at a fully known pad so both figures share one source
     lg1 = 1.0 - (1.0 - float(cmp_rows[-1]["ser_pad"]))
@@ -702,9 +705,11 @@ def stage_J():
         best_kappa = np.empty(trials)
         best_frac = np.empty(trials)
         for t in range(trials):
-            g = rng.standard_normal((K, L))
+            # |first coordinate| of a uniform random unit vector in R^L:
-            g /= np.linalg.norm(g, axis=1, keepdims=True)
+            # its square is Beta(1/2, (L-1)/2), so the best of K draws
-            best_kappa[t] = np.abs(g[:, 0]).max()
+            # needs K scalars rather than K*L Gaussians
+            best_kappa[t] = np.sqrt(rng.beta(0.5, (L - 1) / 2.0,
+                                             size=K).max())
             # permutation: fraction of fixed points, Binomial(d, 1/d) per
             # draw, so the best of K draws is the max of K such counts
             best_frac[t] = rng.binomial(d, 1.0 / d, size=K).max() / d
@@ -726,14 +731,16 @@ def csv_rows(path):
         yield from _csv.DictReader(f)
 
 
-def oma_ser_jammed(snr_db, jsr_db_list, bits=16, U=4, n_grid=4096):
+def oma_ser_jammed(snr_db, jsr_db_list, bits=16, U=4, d=256, n_grid=4096):
     """OMA under a jammer that concentrates on the victim's slots.
 
-    An OMA user occupies d/U exclusive real dimensions that are public,
+    An OMA user occupies L = d/U exclusive real dimensions that are
-    so a jammer needs no key to put all of its power there. With unit
+    public, and drives its 16 index bits on 16 of them with the whole
-    energy per real dimension and a total jammer energy of rho times the
+    allocation energy, an amplitude gain of sqrt(L/bits) per bit. A
-    frame energy, concentrating on d/U of the d dimensions gives a
+    jammer needs no key to put all of its power on those same public
-    per-dimension jammer variance of U*rho.
+    dimensions. With unit energy per real dimension and a total jammer
+    energy of rho times the frame energy, concentrating on bits of the d
+    dimensions gives a per-dimension jammer variance of (d/bits)*rho.
 
     The jammer reaches the victim through its own Rayleigh channel, the
     same convention eval_scheme uses for every simulated scheme, so the
@@ -746,11 +753,12 @@ def oma_ser_jammed(snr_db, jsr_db_list, bits=16, U=4, n_grid=4096):
     hj2 = h2.clone()                           # |hJ|^2 ~ Exp(1), independent
     h = h2.sqrt()[:, None]                     # (n,1) signal amplitude
     snr = 10.0 ** (snr_db / 10.0)
+    gain = math.sqrt((d / U) / bits)                      # antipodal amplitude
     out = []
     for jsr_db in jsr_db_list:
         rho = 10.0 ** (jsr_db / 10.0)
-        var = (1.0 / snr + U * rho * hj2)[None, :]        # (1,n)
+        var = (1.0 / snr + (d / bits) * rho * hj2)[None, :]        # (1,n)
-        arg = (h / var.sqrt()).clamp(0, 38)
+        arg = (h * gain / var.sqrt()).clamp(0, 38)
         pe = 0.5 * torch.erfc(arg / math.sqrt(2.0))       # per-bit error
         out.append(float((1.0 - (1.0 - pe) ** bits).mean()))
     return out
@@ -774,7 +782,8 @@ def stage_L():
     gp = torch.Generator().manual_seed(11)
     perms = torch.randperm(d, generator=gp)[None].repeat(m.users, 1)
     jsr = [float(v) for v in range(-10, 21, 2)]
-    oma = oma_ser_jammed(10.0, jsr, bits=int(math.log2(m.V)), U=m.users)
+    oma = oma_ser_jammed(10.0, jsr, bits=int(math.log2(m.V)),
+                         U=m.users, d=m.d)
     rows = []
     for i, j in enumerate(jsr):
         blind = eval_scheme(m, 10.0, F, jam_w="blind", jsr_db=j)

code/exp_real_sec.py

@@ -29,7 +29,7 @@ import torch
 import sse_lib as L
 from sse_lib import (DATA, DEVICE, SSE, rayleigh_gain, snr_to_sigma2,
                      set_seed, write_csv)
-from exp_full import main_model, eve_wrong_mask
+from exp_full import main_model, eve_wrong_mask, MAIN_D
 
 SNR_GRID = [0, 4, 8, 12, 16, 20, 24, 28]
 # headline recovery is meaningful only where the legitimate user clears
@@ -101,15 +101,22 @@ def wrong_keyed(model: SSE, digits_all, snr_db, seed, rx_masks=None,
 
 
 @torch.no_grad()
-def wrong_oma(ids_all, snr_db, seed, bits=16):
+def wrong_oma(ids_all, snr_db, seed, bits=16, d=256, users=4):
-    """Antipodal signaling on the actual token bits, same frame energy."""
+    """Antipodal signaling on the actual token bits, same frame energy.
+
+    The OMA user owns d/U exclusive dimensions for its 16 bits and puts
+    the whole allocation energy on them, so the antipodal amplitude
+    carries a factor sqrt((d/U)/bits) over the one-bit-per-dimension
+    case. Without it the reference would spend only a quarter of the
+    energy the proposed user spends."""
     torch.manual_seed(seed)
     N, Uu = ids_all.shape
     b = ((ids_all[..., None] >> torch.arange(bits)) & 1).float() * 2 - 1
     b = b.to(DEVICE)
     sigma = math.sqrt(1.0 / (10.0 ** (snr_db / 10.0)))
+    gain = math.sqrt((d / users) / bits)
     h = rayleigh_gain((N, Uu, 1))
-    y = h * b + sigma * torch.randn(N, Uu, bits, device=DEVICE)
+    y = gain * h * b + sigma * torch.randn(N, Uu, bits, device=DEVICE)
     return ((y * b) < 0).any(dim=2).cpu()
 
 
@@ -136,7 +143,7 @@ def main():
           f"distinct tokens, max id {int(ids_all.max())}")
 
     # keys and codebook trained on uniform indices, reused unchanged
-    model = main_model(P=P_MAX, vu=VU, d=64, U=U)
+    model = main_model(P=P_MAX, vu=VU, d=MAIN_D, U=U)
     model.eval()
 
     eve_m = eve_wrong_mask(U, model.L, seed=20260813)          # outsider

code/exp_refresh.py

@@ -26,7 +26,7 @@ orthogonal. Two constructions are compared here.
        the codebook together, which is a relabeling,  log2(L!) bits
     3. a permutation of which user holds which row,   log2(U!) bits
 
-  At L=16 and U=4 that is 16 + 44.25 + 4.58 = 64.8 bits per block, and
+  At L=64 and U=4 that is 64 + 296.0 + 4.58 = 364.6 bits per block, and
   each transformation is verified below to leave the legitimate error
   rate unchanged.
 
@@ -45,7 +45,7 @@ import numpy as np
 import torch
 
 from sse_lib import DATA, DEVICE, SSE, write_csv, eval_ser_sse
-from exp_full import (hadamard, get_model, base_keys, eval_ser_eve,
+from exp_full import (MAIN_D, hadamard, get_model, base_keys, eval_ser_eve,
                       eve_wrong_mask)
 from exp_kpa import collect_known_plaintext, solve_keys
 
@@ -92,7 +92,7 @@ def install(model: SSE, keys: torch.Tensor, codebook: torch.Tensor,
 
 
 def main():
-    P, VU, D, U = 4, 16, 64, 4
+    P, VU, D, U = 4, 16, MAIN_D, 4
     Lp = D // P
     print(f"[K] refresh: L={Lp}, U={U}, "
           f"{entropy_bits(U, Lp):.1f} bits per block from the invariance group")

code/replot_security.py

@@ -129,6 +129,24 @@ def save(fig, name, insets=()):
                         raise RuntimeError(
                             f"{name}: a data curve passes under the legend "
                             f"box; move the legend or shrink it")
+            for t in ax.texts:
+                tb = t.get_window_extent()
+                if (lb.x0 < tb.x1 and tb.x0 < lb.x1
+                        and lb.y0 < tb.y1 and tb.y0 < lb.y1):
+                    raise RuntimeError(
+                        f"{name}: the annotation {t.get_text()!r} sits under "
+                        f"the legend box; move one of them")
+        for t in ax.texts:
+            tb = t.get_window_extent()
+            for line in ax.get_lines():
+                xy = line.get_xydata()
+                if len(xy) == 0:
+                    continue
+                for px, py in ax.transData.transform(xy):
+                    if tb.x0 <= px <= tb.x1 and tb.y0 <= py <= tb.y1:
+                        raise RuntimeError(
+                            f"{name}: a curve is drawn through the "
+                            f"annotation {t.get_text()!r}; move it")
     for ins in insets:
         ib = ins.get_window_extent()
         for a in fig.axes:
@@ -148,6 +166,53 @@ def save(fig, name, insets=()):
     print("[OK]", name)
 
 
+def main_legit(snr_db="10"):
+    """The legitimate SER of the main configuration, read from the curve
+    the main configuration produced rather than looked up by key length."""
+    for r in load("sec_snr.csv"):
+        if float(r["snr_db"]) == float(snr_db):
+            return float(r["legit"])
+    raise KeyError("no %s dB row in sec_snr.csv" % snr_db)
+
+
+def place_legend(ax, cands=("lower left", "center left", "center right",
+                           "lower center", "upper right", "upper center",
+                           "center", "lower right"),
+                 sizes=(6.6, 6.2, 5.8, 5.4, 5.0)):
+    """Choose the location and font size whose box the fewest curve points
+    fall inside, scored on rendered geometry rather than guessed from the
+    data. The size sweep is what makes a long label set placeable: a
+    five-entry legend of full scheme names has no clear corner at the
+    default size on every figure."""
+    best = None
+    for size in sizes:
+        for loc in cands:
+            leg = ax.legend(loc=loc, prop={"size": size})
+            ax.figure.canvas.draw()
+            lb = leg.get_window_extent()
+            hits = 0
+            for line in ax.get_lines():
+                xy = line.get_xydata()
+                if len(xy) == 0:
+                    continue
+                for px, py in ax.transData.transform(xy):
+                    if lb.x0 <= px <= lb.x1 and lb.y0 <= py <= lb.y1:
+                        hits += 1
+            for t in ax.texts:
+                tb = t.get_window_extent()
+                if (lb.x0 < tb.x1 and tb.x0 < lb.x1
+                        and lb.y0 < tb.y1 and tb.y0 < lb.y1):
+                    hits += 50      # an annotation hidden is worse than a
+                    #                 few curve points clipped
+            if best is None or hits < best[2]:
+                best = (loc, size, hits)
+            if hits == 0:
+                ax.legend(loc=loc, prop={"size": size})
+                return best
+    ax.legend(loc=best[0], prop={"size": best[1]})
+    return best
+
+
 def fig_snr():
     r = load("sec_snr.csv")
     x = col(r, "snr_db")
@@ -167,22 +232,8 @@ def fig_snr():
     ax.set_xlabel("SNR (dB)")
     ax.set_ylabel("SER")
     ax.set_xlim(min(x), max(x))
-    ax.legend(loc="lower left")
+    place_legend(ax)
-
+    save(fig, "fig_sec_snr")
-    # the gap is a coding gain of a few percent, invisible against two
-    # decades of SER, so an inset reports it as a ratio
-    lg, om = col(r, "legit"), col(r, "oma")
-    ins = ax.inset_axes([0.57, 0.58, 0.39, 0.25])
-    ins.plot(x, [o / l for l, o in zip(lg, om)], color=C_OMA, lw=1.0,
-             marker="^", ms=2.4, markevery=2)
-    ins.axhline(1.0, color="0.55", lw=0.6, ls="--")
-    ins.set_xlim(min(x), max(x))
-    ins.set_ylim(0.995, 1.105)
-    ins.set_yticks([1.00, 1.05, 1.10])
-    ins.set_xticks([0, 10, 20])
-    ins.tick_params(labelsize=5.2, length=1.8, pad=1.0)
-    ins.set_title("OMA / proposed SER", fontsize=5.6, pad=1.5)
-    save(fig, "fig_sec_snr", insets=[ins])
 
 
 def fig_keylen():
@@ -204,7 +255,7 @@ def fig_keylen():
     ax.set_xscale("log", base=2)
     # the curves sweep the upper-left to lower-right diagonal, leaving the
     # lower-left corner empty
-    ax.legend(loc="lower left")
+    place_legend(ax)
     save(fig, "fig_sec_keylen")
 
 
@@ -228,14 +279,13 @@ def fig_jam():
     ax.plot(x, col(r, "perm_blind"), color=C_EVE, marker="s", ls="-.",
             markevery=(me // 2, me), label=LBL["perm"] + ", blind", **OVER)
     nojam = float(load("sec_jam.csv")[0]["nojam"])
-    ax.axhline(nojam, color=C_OMA, ls=(0, (1, 3)), lw=0.9)
+    ax.axhline(nojam, color=C_OMA, ls=(0, (1, 3)), lw=0.9,
-    ax.text(max(x) - 0.6, nojam + 0.02, LBL["nojam"], ha="right",
+               label=LBL["nojam"])
-            va="bottom", fontsize=7.4, color="#555555")
     ax.set_xlabel("JSR (dB)")
     ax.set_ylabel("SER")
     ax.set_xlim(min(x), max(x))
-    ax.set_ylim(0.2, 1.02)
+    ax.set_ylim(0.8 * nojam, 1.02)
-    ax.legend(loc="center right", bbox_to_anchor=(0.985, 0.47))
+    place_legend(ax)
     save(fig, "fig_sec_jam")
 
 
@@ -257,7 +307,7 @@ def fig_sens():
     ax.set_xlabel("Fraction of the key recovered")
     ax.set_ylabel("Eavesdropper SER")
     ax.set_xlim(0, 1)
-    ax.legend(loc="lower left")
+    place_legend(ax)
     save(fig, "fig_sec_sens")
 
 
@@ -273,13 +323,12 @@ def fig_brute():
                 label=LBL["pad"], **OVER)
     ax.semilogx(x, col(r, "ser_mask"), color=C_LEGIT, marker="o", ls="-",
                 label=LBL["mask"])
-    kl = load("sec_keylen.csv")
+    legit = main_legit()
-    legit = float([q for q in kl if int(q["L"]) == 16][0]["legit_ser"])
     ax.axhline(legit, color=C_OMA, ls=":", lw=0.9, label=LBL["legit"])
     ax.set_xlabel("Number of key guesses $K$")
     ax.set_ylabel("Eavesdropper SER")
-    ax.set_ylim(0.2, 1.05)
+    ax.set_ylim(0.8 * legit, 1.05)
-    ax.legend(loc="lower left")
+    place_legend(ax)
     save(fig, "fig_sec_brute")
 
 
@@ -299,7 +348,7 @@ def fig_real():
     ax.set_xlabel("SNR (dB)")
     ax.set_ylabel("TER")
     ax.set_xlim(min(x), max(x))
-    ax.legend(loc="lower left")
+    place_legend(ax)
     save(fig, "fig_sec_real")
 
 
@@ -325,10 +374,9 @@ def fig_kpa():
         print("[skip] pkpa.csv not present yet")
     # legitimate reference measured with the SAME estimator as the
     # eavesdropper curves, namely the four-user average of eval_ser_sse
-    # at L=16, taken from sec_keylen.csv rather than from the user-1
+    # in the main configuration, rather than the user-1 convention of the
-    # convention of the scheme-comparison table
+    # scheme-comparison table
-    kl = load("sec_keylen.csv")
+    legit = main_legit()
-    legit = float([r for r in kl if int(r["L"]) == 16][0]["legit_ser"])
     ax.axhline(legit, color=C_OMA, ls=":", lw=0.9, label=LBL["legit"])
     ax.set_xlabel("Known-plaintext frames $N$")
     ax.set_ylabel("Eavesdropper SER")
@@ -336,7 +384,7 @@ def fig_kpa():
     # the 0 dB curve sweeps the upper-right, so anchor the legend at the
     # top edge past the steep drops, above every curve at large N
     ax.set_ylim(top=1.18)
-    ax.legend(loc="upper right", bbox_to_anchor=(1.0, 1.04))
+    place_legend(ax)
     save(fig, "fig_sec_kpa")
 
 

code/sse_lib.py

@@ -35,7 +35,7 @@ DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 # ----------------------------------------------------------------------
 # global configuration
 # ----------------------------------------------------------------------
-D = 64          # embedding dimension (real)
+D = 256         # embedding dimension (real)
 U = 4           # users
 VU = 16         # unit codebook size
 P_MAX = 4       # periods for the main configuration, V = 16^4 = 65536

data/kpa.csv

@@ -1,43 +1,43 @@
 snr_db,n_frames,kappa,eve_ser
-0,1,0.2506237943,0.998554125
+0,1,0.12352509,0.99831525
-0,2,0.591645799,0.9303495
+0,2,0.5251209017,0.667321375
-0,3,0.7079962283,0.8537115
+0,3,0.7064555086,0.355686125
-0,4,0.7998725504,0.767802875
+0,4,0.8180372834,0.153838875
-0,5,0.850597313,0.684796
+0,5,0.851283282,0.11704475
-0,6,0.8767687038,0.6361745
+0,6,0.8886688635,0.08632125
-0,8,0.9171553269,0.520736875
+0,8,0.9222010329,0.071005375
-0,10,0.936547631,0.455442125
+0,10,0.9411106989,0.065095875
-0,12,0.9516445503,0.400028875
+0,12,0.9511190057,0.062455
-0,16,0.9666089579,0.344806875
+0,16,0.9647030935,0.05952275
-0,24,0.9785378739,0.3075
+0,24,0.9768752605,0.057202125
-0,32,0.9843035683,0.29181625
+0,32,0.9825294316,0.056090625
-0,48,0.989774394,0.27860675
+0,48,0.9889646709,0.054879875
-0,64,0.9924546674,0.272548625
+0,64,0.9916648567,0.05434675
-10,1,0.3695881277,0.986553125
+10,1,0.1945309106,0.974309875
-10,2,0.880338943,0.55928725
+10,2,0.8957558513,0.09464225
-10,3,0.9474378824,0.4139
+10,3,0.9584526971,0.061422375
-10,4,0.9692240357,0.339236625
+10,4,0.9749164343,0.057618875
-10,5,0.9786081538,0.3095635
+10,5,0.9840320468,0.055860125
-10,6,0.9846392065,0.292632375
+10,6,0.9870633438,0.055293125
-10,8,0.9900667578,0.278431375
+10,8,0.9915206015,0.0545015
-10,10,0.9934557095,0.270305
+10,10,0.993655026,0.054026875
-10,12,0.9948186457,0.267862125
+10,12,0.9947692543,0.05398375
-10,16,0.9963249952,0.264728875
+10,16,0.9961483911,0.05369625
-10,24,0.9976470947,0.26182575
+10,24,0.9975565806,0.0534085
-10,32,0.998370938,0.260598625
+10,32,0.9982031986,0.053507125
-10,48,0.9989489555,0.259409125
+10,48,0.9988671347,0.053128875
-10,64,0.9992221802,0.258898125
+10,64,0.9991624668,0.05316875
-20,1,0.6164694946,0.807168125
+20,1,0.4000679564,0.75409875
-20,2,0.9846389949,0.29904075
+20,2,0.9798189059,0.05720575
-20,3,0.9948147267,0.268233
+20,3,0.9945692539,0.053949375
-20,4,0.9972566783,0.262752625
+20,4,0.9968175337,0.053615375
-20,5,0.9983854383,0.260594375
+20,5,0.9978483543,0.053312375
-20,6,0.9988236457,0.259794
+20,6,0.9984246671,0.053056125
-20,8,0.9991258562,0.259128625
+20,8,0.9989141598,0.053266375
-20,10,0.9993467629,0.258526
+20,10,0.9992120922,0.053062
-20,12,0.9994955555,0.25809525
+20,12,0.9993801698,0.0532745
-20,16,0.9996308014,0.258118625
+20,16,0.9995821282,0.053144375
-20,24,0.9997742459,0.25761825
+20,24,0.9997434661,0.0532995
-20,32,0.9998249143,0.25777975
+20,32,0.9998255745,0.053043125
-20,48,0.9998879731,0.257699625
+20,48,0.9998879209,0.05301875
-20,64,0.9999218643,0.257602125
+20,64,0.9999150276,0.05316375

data/pkpa.csv

@@ -1,17 +1,17 @@
 n_frames,perm_frac,eve_ser
-1,0.2545572917,0.9990345
+1,0.07789713542,0.9998223333
-2,0.7299479167,0.8108416667
+2,0.4416992188,0.8047886667
-3,0.9266927083,0.4801366667
+3,0.8402018229,0.2011701667
-4,0.9885416667,0.3021165
+4,0.9570963542,0.078129
-5,0.9955729167,0.2737706667
+5,0.9874674479,0.058565
-6,0.9997395833,0.2587145
+6,0.998828125,0.05340366667
-7,1,0.2575458333
+7,0.9992838542,0.05337483333
-8,1,0.2575538333
+8,0.9998697917,0.05304583333
-10,1,0.2575
+10,1,0.05286516667
-12,1,0.2576706667
+12,1,0.05297066667
-16,1,0.2577931667
+16,1,0.0530215
-20,1,0.2578143333
+20,1,0.05311583333
-24,1,0.2574975
+24,1,0.05297233333
-32,1,0.2576283333
+32,1,0.05296366667
-48,1,0.2576288333
+48,1,0.05290466667
-64,1,0.2575123333
+64,1,0.052919

data/real_sec_stats.json

@@ -10,22 +10,22 @@
  "headline_runs": 4,
  "recovery": {
   "20": {
-   "legit": 0.22112422997946612,
+   "legit": 0.7507700205338809,
    "eve": 0.0,
    "insider": 0.0,
-   "oma": 0.19815195071868583
+   "oma": 0.6463039014373717
   },
   "24": {
-   "legit": 0.5395277207392197,
+   "legit": 0.8966889117043121,
    "eve": 0.0,
    "insider": 0.0,
-   "oma": 0.5160420944558521
+   "oma": 0.8390657084188912
   },
   "28": {
-   "legit": 0.7804158110882957,
+   "legit": 0.9588039014373717,
    "eve": 0.0,
    "insider": 0.0,
-   "oma": 0.7583418891170431
+   "oma": 0.9319815195071869
   }
  }
 }

data/real_sec_ter.csv

@@ -1,9 +1,9 @@
 snr_db,ter_legit,ter_eve,ter_insider,ter_oma
-0,0.8789390651,0.9999649972,0.9990586747,0.8927576706
+0,0.3971355208,0.9997737319,0.9959146732,0.5232856128
-4,0.6421088687,0.9999537463,0.9975273022,0.6680284423
+4,0.1907565105,0.9995762161,0.9945970678,0.2740881771
-8,0.3651042083,0.9999649972,0.9957384091,0.3898399372
+8,0.08179654372,0.9993636991,0.9939007621,0.123424874
-12,0.1726488119,0.9999537463,0.9944920594,0.1874349948
+12,0.03395771662,0.9992336887,0.9936032383,0.05201666133
-16,0.07386590927,0.9999337447,0.9938557585,0.08109648772
+16,0.01357608609,0.9991099288,0.9934969798,0.02118419474
-20,0.0309237239,0.9999362449,0.9936069886,0.03380395432
+20,0.005565445236,0.9991286803,0.9934507261,0.008630690455
-24,0.01248849908,0.9999324946,0.9934769782,0.01364484159
+24,0.002230178414,0.9991974358,0.9934207237,0.003452776222
-28,0.004962897032,0.999924994,0.9934394752,0.005405432435
+28,0.0008825706056,0.9992486899,0.9934144732,0.001385110809

data/refresh.csv

@@ -1,25 +1,25 @@
 block,legit_invariant,legit_naive,eve_invariant,eve_naive
-0,0.25722,0.9825916667,0.9998433333,0.999975
+0,0.05306083333,0.08038333333,0.9999508333,0.9999683333
-1,0.257695,0.7424366667,0.9997983333,0.998495
+1,0.05273166667,0.1074658333,0.9999333333,0.9998433333
-2,0.2575741667,0.8482983333,0.9999891667,0.9999966667
+2,0.05277666667,0.1060591667,0.9996633333,0.9999733333
-3,0.2574108333,0.7789558333,0.9997116667,0.9998625
+3,0.053255,0.1242133333,0.9999908333,0.9999083333
-4,0.2578316667,0.619975,0.9978516667,0.9999391667
+4,0.0530325,0.1259233333,0.99994,0.99886
-5,0.2575941667,0.6579858333,0.99976,0.9997716667
+5,0.05305,0.1073991667,0.9976566667,0.9998183333
-6,0.2568508333,0.7788175,0.9997783333,0.9999341667
+6,0.05290833333,0.10623,0.9999625,0.9999325
-7,0.2574466667,0.6938383333,0.9999633333,0.9989225
+7,0.05312583333,0.1075391667,0.9999916667,0.9998858333
-8,0.2568958333,0.8399391667,0.9983208333,0.99991
+8,0.05348333333,0.1069425,0.99999,0.9981908333
-9,0.2572158333,0.6549775,0.9999891667,0.9999816667
+9,0.05322833333,0.1036133333,0.9999966667,0.9998841667
-10,0.2575733333,0.4789683333,0.9995841667,0.9995091667
+10,0.05320666667,0.09874583333,0.99966,0.9998675
-11,0.2569025,0.6551183333,0.999925,0.9999741667
+11,0.05308583333,0.0826975,0.999985,0.996885
-12,0.2582966667,0.8479616667,0.9997208333,0.9999233333
+12,0.05284416667,0.1512316667,0.99997,0.9999375
-13,0.2579675,0.6551841667,0.9999758333,0.9996183333
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-14,0.2572158333,0.6582558333,1,0.999915
+14,0.05330416667,0.1010783333,0.9996125,0.99999
-15,0.2578983333,0.4791275,0.9994441667,0.9999233333
+15,0.0528175,0.1194066667,0.99961,0.9998841667
-16,0.2570508333,0.7614066667,0.9998991667,0.9993675
+16,0.05312166667,0.1301383333,0.9990241667,0.9999683333
-17,0.2576233333,0.8045108333,0.9994666667,0.9999475
+17,0.05278,0.1393716667,0.9994875,0.9997066667
-18,0.2571108333,0.8073525,0.9997158333,0.999985
+18,0.05314333333,0.1566191667,0.9998908333,0.9999208333
-19,0.257955,0.6890983333,0.9992433333,0.9994441667
+19,0.05337666667,0.1165041667,0.9990191667,0.9999958333
-20,0.25777,0.5282241667,0.9981,0.9992808333
+20,0.05288,0.1321825,0.99927,0.9996708333
-21,0.2568141667,0.7426725,0.9999566667,0.999965
+21,0.05266333333,0.1290883333,0.9996075,0.9999233333
-22,0.2578066667,0.6547841667,0.9997633333,0.9990433333
+22,0.05324583333,0.09973166667,0.9991875,0.9997808333
-23,0.2575241667,0.68849,0.9998791667,0.99982
+23,0.05300833333,0.1198525,0.9979583333,0.999895

data/refresh_kpa.csv

@@ -1,7 +1,7 @@
 n_frames,ser_same_block,ser_next_block
-2,0.27116875,0.9988428125
+2,0.0910125,0.999634375
-4,0.260861875,0.998726875
+4,0.0540171875,0.999456875
-8,0.2588515625,0.99869375
+8,0.0532996875,0.9995453125
-16,0.2586403125,0.998735625
+16,0.0532596875,0.9995365625
-32,0.2571871875,0.9986965625
+32,0.0530225,0.999555
-64,0.25772125,0.99869375
+64,0.053059375,0.9995303125

data/refresh_summary.csv

@@ -1,4 +1,4 @@
 scheme,legit,eve,entropy_bits
-None (fixed key),0.2573025,0.9999908333,14.99964774
+None (fixed key),0.05300666667,0.9997075,23.76910417
-Fresh orthogonal keys,0.7103737847,0.9996877083,14.99964774
+Fresh orthogonal keys,0.1215548611,0.9996535069,23.76910417
-Invariant,0.2574685069,0.99957,64.83510297
+Invariant,0.05304121528,0.9995528819,364.5801064

data/sec_brute.csv

@@ -1,29 +1,29 @@
 L,K,best_rho,eve_ser
-8,1,0.2918601623,0.9945244994
+8,1,0.2918601623,0.9066920085
-8,10,0.6307837307,0.9550597921
+8,10,0.6307837307,0.5210311818
-8,100,0.8190062809,0.8147158732
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-8,1000,0.9077793813,0.5723297059
+8,1000,0.9077793813,0.07579906172
-8,10000,0.9535904264,0.3866372342
+8,10000,0.9535904264,0.06202610787
-8,100000,0.9757162716,0.3157766639
+8,100000,0.9757162716,0.05732935088
-8,1000000,0.9872786315,0.2845244539
+8,1000000,0.9872786315,0.05494886822
-16,1,0.2228791779,0.9990085712
+16,1,0.2228791779,0.9678674777
-16,10,0.4570522499,0.9943536935
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-16,100,0.6321070191,0.9769163907
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-16,1000,0.7417848118,0.9311708657
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-16,10000,0.8164950053,0.8399011082
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-16,100000,0.8673534005,0.7274791752
+16,100000,0.8673534005,0.0917885764
-16,1000000,0.9048131336,0.5915534824
+16,1000000,0.9048131336,0.07564447448
-32,1,0.1399813941,0.999821061
+32,1,0.1399813941,0.9955028264
-32,10,0.3205750013,0.9991354579
+32,10,0.3205750013,0.9696107631
-32,100,0.4660256564,0.996404209
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-32,1000,0.5620279439,0.991343747
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-32,1000000,0.7479539255,0.9349306487
+32,1000000,0.7479539255,0.2384664588
-64,1,0.0987436915,0.9999096477
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-64,10,0.2263401688,0.9997303409
+64,10,0.2263401688,0.9932755581
-64,100,0.3389944824,0.9991750164
+64,100,0.3389944824,0.9716186298
-64,1000,0.4155608514,0.9981802181
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-64,10000,0.4757575011,0.9967597446
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-64,100000,0.5299797378,0.994275692
+64,100000,0.5299797378,0.7724368738
-64,1000000,0.5741312045,0.9910114047
+64,1000000,0.5741312045,0.6658700504

data/sec_brute_cmp.csv

@@ -1,15 +1,15 @@
 K,ser_mask,ser_perm,ser_pad,best_kappa,best_frac
-1,0.9993544844,0.9999768274,0.9999886992,0.1909643153,0.0160546875
+1,0.9975735971,0.9999452686,0.9999855534,0.1060014706,0.00373046875
-3,0.997243306,0.9999681491,0.9999660976,0.3326517476,0.0281640625
+3,0.9939382519,0.999917898,0.9999566603,0.1737056038,0.006982421875
-10,0.9938504211,0.999957483,0.9998869919,0.450762326,0.043046875
+10,0.9885799467,0.999885267,0.9998555344,0.23239142,0.010859375
-30,0.9845428901,0.9999498125,0.9996609756,0.5560672497,0.05375
+30,0.9784526651,0.9998643897,0.9995666031,0.2887248616,0.01333984375
-100,0.9739208985,0.999941526,0.9988699188,0.6290900875,0.0653125
+100,0.9639408295,0.9998383342,0.9985553436,0.3406099894,0.01643554687
-300,0.9540369033,0.9999351712,0.9966097565,0.688703621,0.0741796875
+300,0.9496426441,0.9998154842,0.9956660309,0.3763731975,0.01915039062
-1000,0.9234069553,0.9999275566,0.9886991882,0.7427389508,0.0848046875
+1000,0.9229213733,0.9997945248,0.9855534363,0.4126070285,0.021640625
-3000,0.884658701,0.9999206979,0.9660975647,0.7822538913,0.094375
+3000,0.8778012069,0.9997791545,0.9566603088,0.4459480988,0.02346679688
-10000,0.8305012761,0.999914875,0.8869918823,0.8169040678,0.1025
+10000,0.8268693567,0.9997572087,0.8555343628,0.4766569871,0.02607421875
-30000,0.7833179277,0.9999088001,0.660975647,0.8434367197,0.1109765625
+30000,0.7835337024,0.9997399479,0.5666030884,0.502465176,0.028125
-65536,0.7451236968,0.9999050488,0.25939,0.8592030095,0.1162109375
+65536,0.7563541371,0.9997287696,0.05323,0.5185642041,0.029453125
-100000,0.7206406422,0.9999030892,0.25939,0.8678447033,0.1189453125
+100000,0.7390554126,0.9997216187,0.05323,0.5289073023,0.03030273438
-300000,0.6546171753,0.9998972103,0.25939,0.8874619916,0.1271484375
+300000,0.6980880931,0.9997044401,0.05323,0.5526416305,0.03234375
-1000000,0.5948033388,0.9998918073,0.25939,0.9034448904,0.1346875
+1000000,0.6655177674,0.9996913713,0.05323,0.5720463815,0.03389648438

data/sec_compare.csv

@@ -1,6 +1,6 @@
 scheme,legit_ser,eve_out,eve_in,jam0_ser
-proposed,0.257845,1,0.9999775,0.7719425
+proposed,0.0529425,0.9999925,0.9999825,0.3634175
-public_mask,0.257845,0.257845,0.257845,0.91675
+public_mask,0.0529425,0.0529425,0.0529425,0.83882
-perm_key,0.2580675,0.99999,0.2580675,0.7721175
+perm_key,0.0528525,0.9999925,0.0528525,0.36425
-index_cipher,0.257845,0.9999847412,0.9999847412,0.91675
+index_cipher,0.0529425,0.9999847412,0.9999847412,0.83882
-oma_plain,0.2747696909,0.2747696909,0.2747696909,nan
+oma_plain,0.08056383667,0.08056383667,0.08056383667,nan

data/sec_jam.csv

@@ -1,8 +1,8 @@
 jsr_db,blind,matched,nojam
--10,0.41313,0.602758,0.257308
+-10,0.100512,0.370894,0.053384
--5,0.583948,0.79449,0.257308
+-5,0.186312,0.629542,0.053384
-0,0.772444,0.917424,0.257308
+0,0.364964,0.838068,0.053384
-5,0.902872,0.97136,0.257308
+5,0.610574,0.94122,0.053384
-10,0.964788,0.990494,0.257308
+10,0.816206,0.980862,0.053384
-15,0.988114,0.99704,0.257308
+15,0.929192,0.993638,0.053384
-20,0.99617,0.999024,0.257308
+20,0.97542,0.997986,0.053384

data/sec_jam_cmp.csv

@@ -1,17 +1,17 @@
 jsr_db,blind,matched,perm_blind,oma_targeted
--10,0.4144466667,0.6026633333,0.4142366667,0.6401244609
+-10,0.10064,0.37023,0.10146,0.5614379461
--8,0.47322,0.6827466667,0.4749333333,0.7152454705
+-8,0.1249233333,0.4708266667,0.12691,0.65563829
--6,0.5445766667,0.7588666667,0.54569,0.7840240868
+-6,0.1634766667,0.5763733333,0.16262,0.7407016397
--4,0.6223533333,0.8241966667,0.6225633333,0.8424432091
+-4,0.214,0.6803433333,0.21294,0.8120662111
--2,0.7009733333,0.8757933333,0.7011833333,0.8888838836
+-2,0.2825866667,0.76744,0.2808,0.8681995366
-0,0.7724933333,0.9165966667,0.7720633333,0.9237966241
+0,0.3646733333,0.8388366667,0.36478,0.9100289945
-2,0.8337533333,0.9441633333,0.8322966667,0.9488822017
+2,0.4608633333,0.8904066667,0.4600566667,0.9398716824
-4,0.88268,0.9645966667,0.88304,0.9662811712
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-6,0.9196833333,0.97682,0.9193566667,0.9780307416
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-8,0.9457333333,0.9852033333,0.9457433333,0.9858109157
+8,0.7445133333,0.96962,0.74325,0.9834284377
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-12,0.97734,0.9942133333,0.9765066667,0.9941743502
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-14,0.98498,0.9962466667,0.9854933333,0.9962827887
+14,0.9134533333,0.99211,0.9136866667,0.9956760816
-16,0.9904633333,0.9975066667,0.99042,0.9976304229
+16,0.94315,0.9950333333,0.9424633333,0.9972471319
-18,0.99391,0.99848,0.99382,0.9984893291
+18,0.96219,0.9969166667,0.96196,0.9982475299
-20,0.99619,0.9990566667,0.9963433333,0.9990359654
+20,0.9756633333,0.99808,0.97557,0.9988837869

data/sec_jam_gap.csv

@@ -1,201 +1,201 @@
 ser,gap_db
-0.6026633333,5.493678481
+0.37023,10.11553523
-0.6046408543,5.495143167
+0.3732723786,10.11830625
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data/sec_maskfam.csv

@@ -1,5 +1,5 @@
 family,legit_ser,eve_ser,eve_ones_ser,mask_xcorr
-random,0.64962,0.998488,0.999988,0.2709003091
+random,0.068322,0.998269,0.999996,0.06645943969
-hadamard,0.257299,0.9999905,0.9999755,0
+hadamard,0.0530375,0.9997025,0.9999785,0
-learned,0.2762895,0.9999285,0.99999,0.007116591092
+learned,0.0635265,0.9997915,0.9997865,0.006678360514
-learned_reg,0.315952,0.9999555,0.9999175,0.01121100038
+learned_reg,0.061412,0.999691,0.9998455,0.002381352475

data/sec_regjam.csv

@@ -1,8 +1,8 @@
 jsr_db,plain,regularized
--10,0.46703,0.4719866667
+-10,0.11818,0.11519
--5,0.6348,0.6301533333
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-20,0.9968333333,0.9964333333
+20,0.97962,0.9787433333

data/sec_sens.csv

@@ -1,23 +1,23 @@
 rho,eve_ser
-0,0.999985
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data/sec_sens_cmp.csv

@@ -1,14 +1,14 @@
 frac,ser_mask,ser_perm,ser_pad
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-1,0.2575758333,0.2577083333,0.25939
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data/sec_snr.csv

@@ -1,12 +1,12 @@
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