Add pd: peering-degree Blend Monte-Carlo graph simulator
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>
2026-08-03 16:47:46 +02:00
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#!/usr/bin/env python3
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Rename the simulator and report from pd to blend
The study started as a peering-degree question and grew well past it: propagation,
adversary exposure, deanonymization and time-to-link, reliability under uniform
and correlated churn, messaging redundancy, and cover traffic. The pd name no
longer describes it.
tools/simulators/blend/pd/ -> tools/simulators/blend/, package src/pd -> src/blend,
and reports/blend/pd/ -> reports/blend/. Moved with git mv so history follows.
The text substitutions are deliberately narrow. pd is also the conventional pandas
alias, and pandas genuinely has a pd.plotting submodule, so a blanket pd. -> blend.
rewrite would have corrupted four files. Only package-unambiguous forms were
changed: from pd.X, -m pd.X, pd.<our module>, PD_BYTES_BUDGET, src/pd, and the
pyproject name. All four import pandas as pd lines are untouched and verified.
Both READMEs reframed: peering degree is now presented as the primary axis that
ties the others together rather than as the subject, and the relative links, which
lost a directory level in the move, are corrected.
Verified after the move: ruff clean, 101 tests, 45 verify anchors, make targets,
the script shims, an end-to-end smoke run, and data/report_numbers.py still
reproducing the report tables from the checked-in evidence.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-06 12:20:07 +02:00
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"""Shim: add src/ to sys.path, then run blend.sweep:main."""
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Add pd: peering-degree Blend Monte-Carlo graph simulator
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>
2026-08-03 16:47:46 +02:00
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import sys
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from pathlib import Path
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sys.path.insert(0, str(Path(__file__).resolve().parents[1] / "src"))
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Rename the simulator and report from pd to blend
The study started as a peering-degree question and grew well past it: propagation,
adversary exposure, deanonymization and time-to-link, reliability under uniform
and correlated churn, messaging redundancy, and cover traffic. The pd name no
longer describes it.
tools/simulators/blend/pd/ -> tools/simulators/blend/, package src/pd -> src/blend,
and reports/blend/pd/ -> reports/blend/. Moved with git mv so history follows.
The text substitutions are deliberately narrow. pd is also the conventional pandas
alias, and pandas genuinely has a pd.plotting submodule, so a blanket pd. -> blend.
rewrite would have corrupted four files. Only package-unambiguous forms were
changed: from pd.X, -m pd.X, pd.<our module>, PD_BYTES_BUDGET, src/pd, and the
pyproject name. All four import pandas as pd lines are untouched and verified.
Both READMEs reframed: peering degree is now presented as the primary axis that
ties the others together rather than as the subject, and the relative links, which
lost a directory level in the move, are corrected.
Verified after the move: ruff clean, 101 tests, 45 verify anchors, make targets,
the script shims, an end-to-end smoke run, and data/report_numbers.py still
reproducing the report tables from the checked-in evidence.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-06 12:20:07 +02:00
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from blend.sweep import main # noqa: E402
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Add pd: peering-degree Blend Monte-Carlo graph simulator
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>
2026-08-03 16:47:46 +02:00
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if __name__ == "__main__":
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raise SystemExit(main())
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