f51630e509
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>
Evidence of record
The sweep outputs behind every number in the report. The simulator does not commit
its own runs/ directory — these are the copies of record, kept so that any figure or table can be
re-derived, or challenged, without re-running hours of compute.
Each run directory holds the three tables the simulator writes: propagation.parquet,
adversary.parquet and deanon.parquet.
| directory | config | sampling | backs |
|---|---|---|---|
default/ |
configs/default.yaml |
1 000 rounds × 8 seeds = 8 000/cell | §3.1–§3.5 — delay, observation, eclipse, deanonymization, delivery, coverage |
redundancy/ |
configs/redundancy.yaml |
1 200 × 8 = 9 600/cell | §3.8 — messaging redundancy R = 1…4 |
percolation/ |
configs/percolation.yaml |
800 × 8 = 6 400/cell | §3.5 — the churn threshold u_c = 1 − 1/(degree − 1) |
correlated-churn/ |
configs/correlated-churn.yaml |
800 × 8 = 6 400/cell | §3.9 — correlated AS/region outages vs uniform churn |
fullscale/ |
configs/fullscale.yaml |
64 × 3 = 192/cell | §5 — the 10⁶ scaling check (deliberately lighter; not a source of headline numbers) |
cover-traffic/ |
configs/cover-traffic.yaml |
900 s timeline × 4 seeds | §3.10 — blending, mixing, and the emission-quota stake ceiling. Carries a fourth table, traffic.parquet |
The linkability results (§3.6–§3.7) and both deanonymization rates are closed forms over these
tables rather than separate measurements, so they have no run of their own — blend.linkability
derives them and make verify checks them against Monte-Carlo.
Regenerating the report's numbers
python report_numbers.py
prints every quoted value with its across-topology standard error, straight from the parquets here.
That is the fastest way to check a table in the report against its evidence. It takes optional
paths (report_numbers.py <default> <redundancy> <percolation>) if you want to point it at fresh
runs instead.
Regenerating the data itself
From tools/simulators/blend: make sweep,
make redundancy, make percolation, make correlated-churn, make sweep-fullscale. Results
land in that simulator's runs/<timestamp>_<label>/. Note that the seed streams depend on the
configuration, so re-running reproduces the statistics, not bit-identical numbers, unless the
config is unchanged — in which case it does reproduce exactly.
Two runs from the same session are deliberately not kept: the smoke runs (throwaway, far too
noisy to interpret) and an earlier 144-rounds/cell redundancy grid that was superseded because its
sampling error produced a non-monotonic delivery curve — the reason redundancy/ samples 9 600.