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9b03a68a84
Extends the pd Blend simulator along two axes the deanonymization model opened up, adds the reports/blend/pd report of record, and fixes three correctness defects found while reviewing the result. Linkability over time (pd.linkability): - time to link an emitter ~ 30s*ln(1/(1-alpha))/(stake*q): inversely proportional to stake, so a 5% staker is linked in ~2 days and a 0.001% staker only after ~27 years; - time to certify a node's stake >= theta from the count of attributable observations (relative precision ~1/sqrt(N)): sizing a node costs 100-400x more than identifying it, and sub-0.1% stake is practically unlearnable. Both are closed forms over the exact deanonymization rates and a stake-proportional 30 s emission cadence, checked against a Monte-Carlo of the emission process in verify. Messaging redundancy (R independent cascades per emission, R = 1..4): - `redundancy` knob threaded through config/rng/propagation/engine/metrics/ sweep; a node receives from whichever cascade reaches it first, so arrival times combine element-wise. Delivery and capture both follow 1-(1-x)^R, so redundancy trades reliability against anonymity and divides time-to-link by ~R. Measured: delivery 0.34 -> 0.81 at 30% churn for R = 1 -> 4, while a 1%-staker's time to link falls 10 d -> 2.5 d. - Redundancy buys NO coverage: a cascade only delivers if the sender could already route to its relay, so every delivered cascade floods the sender's own component. Coverage is flat in R to four decimals at every degree. - Near the percolation threshold the cascades fail together rather than independently, so redundancy under-delivers against 1-(1-p1)^R there. Churn percolation (configs/percolation.yaml, verify check 7): - the flood only crosses responsive nodes, so it lives on the responsive sub-graph -- site percolation on a d-regular graph. A network survives churn only up to u_c = 1 - 1/(degree-1); measured collapse lands on the predicted threshold for every degree (3 -> 0.50, 6 -> 0.80, 16 -> 0.93), which inverts into the sizing rule degree > 1 + 1/(1-u). Correctness fixes: - redundancy delay used the fastest cascade's own full delay, which over-states it (min-max vs max-min); now the element-wise earliest arrival, reducing exactly to the single-cascade model at R = 1 (test); - the "redundancy improves coverage" claim was false in both the report and the simulator README -- removed and replaced with the measured result; - per-hop latency is degree-dependent (1.5 s at degree 16 to 2.7 s at degree 3), not a flat 1.6 s; and the worst-case observation figure was averaged over degrees -- at degree 8 and f_adv = 0.2 it is 0.83 -> 1.000. Statistics: round counts raised for resolution rather than speed -- 8000 rounds per cell in the main sweep, 9600 in the redundancy study, 6400 in the percolation study, giving SEM <= 0.009 on every delivery rate and <= 0.04 s on every delay mean. The previous redundancy grid (144 rounds/cell) produced a non-monotonic delivery curve; it is now monotonic and within 0.015 of theory. Adversary and deanonymization metrics remain closed-form and exact. reports/blend/pd: the report of record -- peering-degree trade-offs across speed, observation, eclipse, deanonymization and reliability, plus the time-to-link, stake-inference, redundancy and churn-threshold sections, with 21 figures of record and an explicit sampling-error statement. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
24 lines
1.0 KiB
YAML
24 lines
1.0 KiB
YAML
# Messaging-redundancy study: R independent blend cascades per emission (R in 1..4).
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# Measures delivery_R (reliability) and deanon_R / full_deanon_R (anonymity cost), and feeds the
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# time-to-link / stake-inference figures.
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#
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# Statistics: 1200 rounds x 8 topology seeds = 9600 rounds per cell -> binomial SEM <= 0.005 on
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# every delivery rate, enough to resolve the 1-(1-p)^R law (a coarser grid was non-monotonic in R).
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# degree 3 is included because it is the one setting where the responsive sub-graph fragments, so
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# it tests whether redundancy can buy back coverage (it cannot -- see the report).
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# max_blend_delay=0 keeps it affordable: redundancy multiplies propagation cost by R, and mixing
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# delays are not the subject here (the headline delay study is configs/default.yaml).
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n_nodes: [20000]
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degree: [3, 8]
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blend_hops: [1, 3]
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max_blend_delay: [0]
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unresponsive_frac: [0.0, 0.1, 0.2, 0.3, 0.5]
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redundancy: [1, 2, 3, 4]
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f_adv: [0.1, 0.2, 0.33]
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adversary_mode: [random, worstcase_coverage]
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seeds: 8
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base:
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n_rounds: 1200
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n_placements: 4
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worstcase_max_n: 100000
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