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This analysis shows how coordinated rogue nodes can increase public key exposure risk in Bitcoin networks, suggesting new monitoring techniques.
Bitcoin transaction relay is decentralized and governed by locally defined, non consensus policies that are neither globally enforced nor externally observable as explicit configuration. While the effects of these policies may in principle be indirectly inferred through relay metadata only under aggregate adversary level observation accumulated over time and with significant uncertainty, their opacity results in probabilistic and heterogeneous propagation behavior. Each node maintains an independent mempool and propagates transactions according to local rules and network conditions. Under hypothetical cryptographically relevant quantum computer (CRQC) assumptions, transaction relay behavior becomes a relevant factor in public key exposure risk. This paper presents a defensive threat model in which an adversary operates a coordinated network of protocol compliant Bitcoin peer to peer nodes to probabilistically obtain preferential locally first seen visibility at standard broadcast time into transactions that reveal public key material. Observation occurs only upon normal transaction broadcast from ordinary network nodes and is limited to what individual nodes receive and infer relative to their connected peers and local mempool state. The model relies on probabilistic network positioning rather than pre broadcast access, protocol deviation, or relay interference. The analysis does not assume deterministic control of relay, miner collusion, eclipse attacks, privileged network access, or present day quantum feasibility. Instead, it formalizes how partial node local observability, relay variance, and adaptive analytical selection may increase expected adversarial value under uncertainty while remaining indistinguishable from benign network participation. A quantitative expected value framework and a lightweight simulation are introduced to bound adversarial advantage as a function of network coverage, propagation delay variance, and cryptographic feasibility. The work is grounded in prior empirical measurement literature and is intended to inform defensive monitoring and mitigation research rather than predict specific attacks or timelines.
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Michael Strike (2026) studied this question.
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