Research map / Image generation / Autoregressive and latent generation
Deanonymization in the Bitcoin P2P Network
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| switch from trickle to diffusion spreading | Bitcoin flooding protocol · d-regular trees, first-timestamp estimator | probability of detection | No clear effect |
| use ML/graph-aware estimator instead of first-timestamp estimator (trickle) | eavesdropper adversary · d-regular trees | probability of detection | Helped |
| increase node degree d | first-timestamp estimator · d-regular trees | probability of detection | Hurt |
| increase node degree d | ML estimator · d-regular trees | probability of detection | No clear effect |
| use reporting centrality instead of first-timestamp estimator (diffusion) | eavesdropper adversary · d-regular trees, d >= 9 | probability of detection | Helped |
| use reporting centrality instead of first-timestamp estimator (diffusion) | eavesdropper adversary · Bitcoin graph, approx 8-regular | probability of detection | No clear effect |
| increase adversary connections per server theta | first-timestamp estimator · 4-regular trees | probability of detection | Mixed |
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