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Static-graph simulator quantifying how a node's peering degree trades off propagation speed, adversary exposure, deanonymization, and reliability in the Blend network. Scales to 1e6 nodes (sparse CSR + sampled Dijkstra); the adversary and deanonymization metrics are exact at every N. Model (ms): seeded d-regular peer graph (matching-union), Blend cascade (sender -> blend_hops timed-release mix relays -> final flood), geographic link base + exponential transport jitter, per-node processing lag, free-running release-clock mixing. Metrics: - propagation: full-delay mean/p50/p90/p99, path/broadcast split, coverage times - reliability: message success-delivery-rate ~ (1-unresponsive_frac)^blend_hops and flood coverage, with unresponsive nodes modelled as routing holes - adversary (exact): observed/eclipsed fractions, random + worst-case placement - deanonymization (exact): P(whole blend path adversarial) ~ f_adv^blend_hops, and full deanonymization (path adversarial AND honest sender peered with an adversary) = deanon_rate * observed_frac Deterministic blake2b seed streams, three parquet tables, joblib parallelism, memguard, an analytic verify harness, 50 unit tests, and an auto-installing Makefile. Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
25 lines
649 B
Python
25 lines
649 B
Python
import numpy as np
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from pd.mixclock import mean_residual_ms, mix_wait
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def test_zero_max_delay_is_zero():
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rng = np.random.default_rng(0)
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w = mix_wait(rng, 0, 1000)
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assert np.all(w == 0.0)
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def test_residual_within_bounds():
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rng = np.random.default_rng(1)
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m = 5
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w = mix_wait(rng, m, 100000)
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assert w.min() >= 0.0
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assert w.max() <= m * 1000.0 + 1e-6 # residual within a covering interval (<= M seconds)
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def test_mean_matches_analytic():
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rng = np.random.default_rng(2)
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for m in (1, 3, 8):
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w = mix_wait(rng, m, 400000)
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assert abs(w.mean() - mean_residual_ms(m)) < 0.03 * mean_residual_ms(m)
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