research/reports/blend/data

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
attribution/	configs/attribution.yaml	closed-form + 4 seeds	§3.4 — the attribution bracket at the report's scale: local confidence, attributable fractions, upstream hops, neighbourhood confidence
timing/	configs/timing.yaml	120 s timeline × 3 seeds	§3.11 — the two release designs under a timing attack, and the minimum-interval control

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 straight from the parquets here -- the §3.1–§3.5 and §3.8 tables with their across-topology standard errors, and the §3.9–§3.11 tables and the §3.4 attribution bracket from their own runs. 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>/.

On exact reproduction. Seed streams are derived from the configuration, so re-running a study reproduces its statistics rather than bit-identical numbers whenever that derivation has moved. It has moved once since these runs: adding failure domains (§3.9) put n_regions and region_locality into the topology seed, which changes the graph drawn for every config, including those leaving both at their defaults. default/, percolation/ and redundancy/ were produced before that change and so no longer reproduce bit-exactly — a spot-checked cell moved by 0.3 %, about 1.5 standard errors, which is ordinary variation between independent realisations rather than a change in behaviour. correlated-churn/, cover-traffic/, fullscale/, timing/ and attribution/ were produced by the current derivation. Quantities that are closed-form (deanon_rate, the quota ceiling, the confidence formulae) depend only on counts and are identical either way; the Monte-Carlo ones scatter within their stated error bars.

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.