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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>
63 lines
2.3 KiB
Python
63 lines
2.3 KiB
Python
from itertools import combinations
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import numpy as np
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from pd.adversary import _greedy_coverage, adversary_metrics, place_adversary
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from pd.config import SimConfig
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from pd.graph import Graph, build_graph
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def _cycle4():
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indptr = np.array([0, 2, 4, 6, 8], dtype=np.int64)
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indices = np.array([1, 3, 0, 2, 1, 3, 0, 2], dtype=np.int64)
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return Graph(n=4, degree=2, indptr=indptr, indices=indices,
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base=np.ones(8), src=np.array([0, 0, 1, 1, 2, 2, 3, 3]), p=np.zeros(4))
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def test_coverage_eclipse_hand_checked():
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g = _cycle4() # 0-1-2-3-0
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m = adversary_metrics(g, np.array([False, True, False, True])) # adv {1,3}
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assert m["observed_count"] == 2 and m["eclipsed_count"] == 2 # honest {0,2} fully surrounded
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m = adversary_metrics(g, np.array([False, True, False, False])) # adv {1}
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assert m["observed_count"] == 2 and m["eclipsed_count"] == 0 # {0,2} observed, none eclipsed
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def test_random_closed_form():
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g = build_graph(SimConfig(n_nodes=5000, degree=6, graph_seed=0))
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rng = np.random.default_rng(0)
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def _obs():
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return adversary_metrics(g, place_adversary(g, 0.2, "random", rng, 10**9))["observed_frac"]
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obs = np.mean([_obs() for _ in range(5)])
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assert abs(obs - (1 - 0.8 ** 6)) < 0.02
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def test_worstcase_coverage_is_an_envelope():
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g = build_graph(SimConfig(n_nodes=400, degree=4, graph_seed=0))
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rng = np.random.default_rng(0)
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rand = adversary_metrics(g, place_adversary(g, 0.2, "random", rng, 10**9))["observed_frac"]
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wc = adversary_metrics(
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g, place_adversary(g, 0.2, "worstcase_coverage", rng, 10**9))["observed_frac"]
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assert wc >= rand - 1e-9
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def test_greedy_coverage_near_optimal():
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g = build_graph(SimConfig(n_nodes=10, degree=3, graph_seed=0))
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best = max(adversary_metrics(g, _mask(10, c))["observed_count"]
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for c in combinations(range(10), 2))
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idx = _greedy_coverage(g, 2, np.random.default_rng(0))
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got = adversary_metrics(g, _mask(10, idx))["observed_count"]
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assert got >= (1 - 1 / np.e) * best - 1e-9
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def _mask(n, idx):
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m = np.zeros(n, dtype=bool)
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m[list(idx)] = True
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return m
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def test_worstcase_cap_raises():
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import pytest
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g = build_graph(SimConfig(n_nodes=200, degree=4))
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with pytest.raises(ValueError):
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place_adversary(g, 0.2, "worstcase_coverage", np.random.default_rng(0), worstcase_max_n=100)
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