Two gaps left by the previous review.
Section 3.4 quoted the attribution bracket at N=20,000 while the only committed
evidence carrying those columns was the timing run at N=2,000, so a reader diffing
report against data saw different numbers for the same quantity. Added
configs/attribution.yaml and a make target: it records both bounds and the graph
hop distance at the reported scale, cheaply, since the adversary and
deanonymization metrics are closed-form and the hop distance is a property of the
topology. It reproduces the section exactly -- L = 2.58 and neighbourhood
confidence 0.640 at degree 8, f_adv 0.2.
It also surfaces a result the smaller run could not: degree cuts both ways. A
sparser graph has longer routes, so it offers the adversary more upstream places
to see the message -- L is 4.18 at degree 4 against 1.93 at degree 16, lifting
neighbourhood confidence from 0.61 to 0.72. The low diameter that makes
propagation fast also starves the adversary, one of the few places where raising
the degree helps anonymity rather than hurting it.
Section 3.11 was the only section without a figure. Fig 25 plots MAP success
against the effective anonymity set for both release designs: the dashed sets
separate far faster than the solid best-guess curves, which is the whole argument
for not trusting perplexity alone.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Second review pass, three findings.
Pending cancellations were a set, so a node that proposed twice before its next
cover emission forfeited only one and then over-emitted relative to its quota --
the precise uniformity cover traffic exists to preserve. Now a multiset.
The timeline drew the block proposer uniformly while quota.py used a stake-
weighted lottery, so the two halves of the cover-traffic model disagreed. The
timeline now takes the stake array. Concentration is visible in the bookkeeping: a
dominant proposer wins most proposals but rarely draws a cover slot to forfeit, so
cancellations redeemed fall from 107 to 28 -- the unredeemed backlog being exactly
the over-emission the stake ceiling describes.
data/report_numbers.py claimed to print every quoted value but covered only
sections 3.1-3.5 and 3.8. Extended to 3.9 correlated churn, 3.10 blending, mixing
and the quota ceiling, 3.11 the release designs, and the 3.4 attribution bracket;
the claim in data/README is corrected to say what it actually does.
Neither model fix moves a published number: the proposer identity does not enter
blending, mixing or timing, and repeat proposals are rare at the reported rates.
Two regression tests pin both.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Review finding: the timing study and the neighbourhood-confidence numbers were
produced by ad-hoc analysis, not by the simulator. timing_linkability,
neighbourhood_confidence and mean_upstream_hops had no callers outside their own
modules; min_blend_delay and release_mode were declared on SweepConfig, validated
and keyed, but never read by sweep.py, so a YAML setting them was silently
ignored; and propagation.py called mix_wait without the minimum, leaving the knob
inert on the delay tables of 3.1-3.2. Section 6 promised every number was
reproducible and data/README claimed to hold the evidence behind every number --
both were false for 3.11.
Now wired end to end: release_designs() is a real sweep axis, the engine measures
the timing attack per design and records it in traffic.parquet, and the deanon
table carries the full attribution bracket (local confidence, attributable
fractions, upstream hops, neighbourhood confidence). Added configs/timing.yaml
and a make target.
The committed sweep reproduces 3.11: MAP success 0.993/0.905/0.683 for clock and
0.989/0.832/0.550 for jitter across the swept rates, and the minimum interval
changes nothing (0.993 vs 0.993). Evidence checked in under data/timing.
Three regression tests pin the wiring so a measure cannot go back to living only
in analysis.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
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>
