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 dataclasses
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import pytest
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from pd.config import SimConfig, SweepConfig
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def test_key_covers_every_field():
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fields = [f.name for f in dataclasses.fields(SimConfig)]
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assert len(SimConfig().key()) == len(fields)
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pd: correlated AS/region churn, and two report caveats corrected
Uncorrelated churn alone was incomplete: real outages take out a datacentre, AS
or region as a unit. Adds failure domains and a correlated churn mode, plus the
metric needed to tell the two apart.
- n_regions / region_locality: nodes belong to equal-sized failure domains, and
a configurable share of each node peers inside its own domain. Locality is what
makes a failure domain a connectivity domain -- with region-blind peering,
dropping whole regions removes a uniformly random set of nodes and is
indistinguishable from uniform churn. The locality matchings keep the graph
exactly d-regular (they change where peers are, never how many).
- churn_mode = uniform | regional, swept per topology so both modes are compared
on the same graph at an identical dead-node count.
- frac_reached_live: coverage of the *responsive* network, alongside coverage of
all nodes. The two move in opposite directions under correlated failure, so one
number could not express the result.
Measured (degree 4, 20 domains, 75% locality, half the network dead): clustered
failure leaves the survivors fully connected -- live coverage 1.000 and delivery
equal to the live-relay rate, i.e. nothing lost to routing -- where the same
number of scattered failures gives 0.857 live coverage and loses delivery to
broken routes. Correlated outages are gentler on the survivors than uniform
churn, while stranding the dead domains. Verify check 8 anchors this.
Also, per review of the caveats: exact d-regularity is a protocol requirement
rather than a modelling simplification, and the timing-correlation adversary is
deferred because it is only meaningful once the network emits cover traffic,
which this simulator does not yet do.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-05 11:37:56 +02:00
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# n_regions=2 in the base so the region/churn fields can each be varied on their own
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# (region_locality and churn_mode="regional" both require n_regions >= 2)
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base = SimConfig(n_regions=2)
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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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for name in fields:
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cur = getattr(base, name)
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pd: correlated AS/region churn, and two report caveats corrected
Uncorrelated churn alone was incomplete: real outages take out a datacentre, AS
or region as a unit. Adds failure domains and a correlated churn mode, plus the
metric needed to tell the two apart.
- n_regions / region_locality: nodes belong to equal-sized failure domains, and
a configurable share of each node peers inside its own domain. Locality is what
makes a failure domain a connectivity domain -- with region-blind peering,
dropping whole regions removes a uniformly random set of nodes and is
indistinguishable from uniform churn. The locality matchings keep the graph
exactly d-regular (they change where peers are, never how many).
- churn_mode = uniform | regional, swept per topology so both modes are compared
on the same graph at an identical dead-node count.
- frac_reached_live: coverage of the *responsive* network, alongside coverage of
all nodes. The two move in opposite directions under correlated failure, so one
number could not express the result.
Measured (degree 4, 20 domains, 75% locality, half the network dead): clustered
failure leaves the survivors fully connected -- live coverage 1.000 and delivery
equal to the live-relay rate, i.e. nothing lost to routing -- where the same
number of scattered failures gives 0.857 live coverage and loses delivery to
broken routes. Correlated outages are gentler on the survivors than uniform
churn, while stranding the dead domains. Verify check 8 anchors this.
Also, per review of the caveats: exact d-regularity is a protocol requirement
rather than a modelling simplification, and the timing-correlation adversary is
deferred because it is only meaningful once the network emits cover traffic,
which this simulator does not yet do.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
2026-08-05 11:37:56 +02:00
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alt = {"n_nodes": 2000, "degree": 4, "n_regions": 4, "region_locality": 0.5,
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"blend_hops": 2, "max_blend_delay": 5,
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"unresponsive_frac": 0.2, "churn_mode": "regional", "redundancy": 2,
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"n_rounds": 10, "transport_jitter_mean_ms": 1.0,
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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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"processing_lags_ms": (11.0, 51.0, 101.0), "processing_lag_probs": (0.6, 0.3, 0.1),
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"link_latency_dist": "fixed", "link_latency_mean_ms": 1.0,
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"coverage_pcts": (25.0,), "f_adv": 0.1, "adversary_mode": "worstcase_coverage",
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"n_placements": 1, "worstcase_max_n": 5, "graph_seed": 99, "replicate": 1,
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"root_seed": 7}[name]
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assert alt != cur
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assert dataclasses.replace(base, **{name: alt}).key() != base.key(), name
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@pytest.mark.parametrize("kw", [
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{"n_nodes": 999}, # odd
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{"degree": 1000}, # >= n
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{"blend_hops": 0}, # < 1
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{"f_adv": 1.0}, # >= 1
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{"max_blend_delay": -1},
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{"processing_lag_probs": (0.5, 0.4)}, # doesn't sum to 1 (with default 3 lags -> len mismatch)
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{"link_latency_dist": "bogus"},
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{"adversary_mode": "bogus"},
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])
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def test_validation_rejects(kw):
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with pytest.raises(ValueError):
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SimConfig(**kw)
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def test_sweep_grids_and_collapse():
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sw = SweepConfig(n_nodes=[1000, 10000], degree=[4, 8], blend_hops=[2, 3],
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max_blend_delay=[0, 3], f_adv=[0.0, 0.2],
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adversary_mode=["random", "worstcase_coverage"], seeds=3)
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assert len(sw.graph_cells()) == 2 * 2 * 3
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assert len(sw.prop_grid()) == 2 * 2
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# f_adv=0 collapses to a single (mode-irrelevant) row; f_adv=0.2 keeps both modes
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assert sw.adv_grid() == [(0.0, "random"), (0.2, "random"), (0.2, "worstcase_coverage")]
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def test_from_dict_rejects_unknown():
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with pytest.raises(ValueError):
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SweepConfig.from_dict({"nonsense": [1]})
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def test_base_config_coerces_tuples():
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sw = SweepConfig(base={"processing_lags_ms": [10.0, 90.0], "processing_lag_probs": [0.3, 0.7]})
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cfg = sw.base_config(1000, 8, 0)
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assert cfg.processing_lags_ms == (10.0, 90.0)
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assert isinstance(cfg.key(), tuple)
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