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 a/d estimator credits the adversary only with the sender own links, which
understates it: to rule out that X was forwarding it need not hold X incoming
link, only have seen the message anywhere upstream. That gives
confidence = 1 / (1 + (1-f_adv)^L)
for L upstream hops, with the local model as the L=1 case.
L is not free -- the graph fixes it. A forwarder sits about halfway along a route
and the peer graph is low-diameter: mean hop distance 5.15 at degree 8, so L ~ 2.6.
Confidence rises from 0.56 to 0.64 at f_adv = 0.2, but 0.9 would need ~10 upstream
hops. The low diameter that makes propagation fast is exactly what starves the
adversary of observation points.
So the bracket closes near the local model rather than near certainty, and the
binary full_deanon treatment is NOT rescued by neighbourhood effects. Both ends
are reported rather than one being chosen, since confident attribution is a
threshold question: an adversary content with 0.64 attributes most senders, one
demanding 0.9 attributes almost none.
What remains unmodelled is an adversary combining this structural evidence with
the timing evidence of 3.11.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
The jitter-vs-clock verdict rested on handing the clock adversary the tick
schedule, which is the conservative choice but also the harsher one -- a real
observer sees only ticks that released something. Rather than assume, both are
now measurable, and they are provably equivalent.
A silent tick implies nothing was pending at it, and any arrival older than the
previous release has demonstrably already left. So the candidate window bounded by
the true previous tick and the one bounded by the previous observed release hold
the same arrivals. Confirmed identical to 1e-9 in both the sparse regime (one hold
per relay) and the dense one (56 holds per relay), where the two coincide for
different reasons.
That removes the objection to the comparison: jitter beats the clock at a matched
delay budget on merit, not because the clock was given a stronger opponent.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Caught by the test immediately: key() enumerates every SimConfig field, so
min_blend_delay and release_mode needed alternative values.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Two release designs at a MATCHED delay budget, so they differ only in how they
delay: clock (batch at free-running ticks, the existing model) and jitter (each
message waits its own exponential draw, mean set equal to the clock residual).
Plus min_blend_delay, which forbids intervals shorter than it.
Minimum interval -- a negative result, and provably so. A zero-length gap is
instantaneous, so it never covers an arrival and is never sampled by the residual
or by the size-biased interval. Excluding it therefore leaves the mean hold
exactly unchanged, and with it blending and linkability; what it does change is
E[S], the gap between release opportunities. Confirmed analytically and in
simulation: 1.168s vs 1.167s at M=3.
Timing attack -- the effective anonymity set of a release (perplexity of the
observer posterior over which arrival produced it), plus MAP success, the chance
its single best guess is right. The second matters because perplexity flatters a
heavy tail: an exponential never fully excludes an old arrival, so it can look
unlinkable while still being guessed correctly.
At the baseline rate BOTH designs fail almost completely -- MAP success 0.98-0.99,
effective set ~1. A relay handles so little traffic that in->out matching is
trivial, which follows directly from the mixing~0 result. Traffic, not delay, is
what buys timing protection.
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
full_deanon counted any sender with an adversarial peer as identified. Capturing a
cascade tells the adversary WHICH message it is following, not WHO started it:
seeing an honest X transmit is equally consistent with X having received it from a
peer the adversary cannot watch. Separating the two gives
confidence = 1/(2 - a/d) = d/(2d - a)
for a adversarial peers of degree d. The path length does not enter -- the
conditioning event already fixes the relays as adversarial, so an honest X is not
one of them for this message.
The consequence is large. One peer of eight is worth 0.53, barely above the 0.5
prior, and 90% confidence needs a >= 8: every peer, which is the ECLIPSE condition
rather than the observation condition. Measured, attributable_frac_90 equals
eclipsed_frac exactly. At f_adv = 0.2, degree 8 that is 2.6e-6 against an
observed_frac of 0.83 -- the published figure overstates confident origination by
five orders of magnitude.
Stated in the report as a bracket rather than a replacement: full_deanon is the
upper bound on adversary capability, this is the lower bound, and the truth lies
between because the adversary also learns from the sender neighbourhood. Closing
that gap needs a k-hop observability model and is recorded as open in section 5.
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>
