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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>
18 lines
179 B
Plaintext
18 lines
179 B
Plaintext
# Generated artifacts
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runs/
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results/
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figures/
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!results/.gitkeep
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# Python
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__pycache__/
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*.py[cod]
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*.egg-info/
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.venv/
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venv/
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.pytest_cache/
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.mypy_cache/
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.ruff_cache/
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*.parquet
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*.csv
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