research/tools/simulators/blend/configs/attribution.yaml

# Attribution confidence at the scale the report quotes (section 3.4).
#
# Capturing a cascade identifies the MESSAGE, not the originator: an honest node seen transmitting
# may simply have been passing one along. This run measures both ends of the resulting bracket.
#
#   local        -- confidence d/(2d-a) from the sender's own a adversarial peers, and the share of
#                   honest nodes attributable at 0.5 / 0.9 / 0.99.
#   neighbourhood -- 1/(1+(1-f)^L), where the adversary also sees the message anywhere upstream.
#                    L is fixed by the graph, so upstream_hops is recorded per run.
#
# Propagation is not the subject, so n_rounds is minimal: the adversary and deanonymization metrics
# are closed-form and exact, and the hop distance is a property of the topology.
n_nodes: [20000]
degree: [4, 8, 16]
blend_hops: [3]
max_blend_delay: [3]
unresponsive_frac: [0.0]
f_adv: [0.1, 0.2, 0.33, 0.5]
adversary_mode: [random, worstcase_coverage]
seeds: 4
base:
  n_rounds: 10
  n_placements: 4
blend: attribution evidence at the reported scale, and a figure for the timing study 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> 2026-08-06 17:12:33 +02:00			`# Attribution confidence at the scale the report quotes (section 3.4).`
			`#`
			`# Capturing a cascade identifies the MESSAGE, not the originator: an honest node seen transmitting`
			`# may simply have been passing one along. This run measures both ends of the resulting bracket.`
			`#`
			`# local -- confidence d/(2d-a) from the sender's own a adversarial peers, and the share of`
			`# honest nodes attributable at 0.5 / 0.9 / 0.99.`
			`# neighbourhood -- 1/(1+(1-f)^L), where the adversary also sees the message anywhere upstream.`
			`# L is fixed by the graph, so upstream_hops is recorded per run.`
			`#`
			`# Propagation is not the subject, so n_rounds is minimal: the adversary and deanonymization metrics`
			`# are closed-form and exact, and the hop distance is a property of the topology.`
			`n_nodes: [20000]`
			`degree: [4, 8, 16]`
			`blend_hops: [3]`
			`max_blend_delay: [3]`
			`unresponsive_frac: [0.0]`
			`f_adv: [0.1, 0.2, 0.33, 0.5]`
			`adversary_mode: [random, worstcase_coverage]`
			`seeds: 4`
			`base:`
			`n_rounds: 10`
			`n_placements: 4`