Bitcoin Under Stress: Measuring Infrastructure Resilience 2014-2025
This addresses the problem of hidden infrastructure dependencies in decentralized systems like Bitcoin, providing empirical evidence for network operators and policymakers, though it is incremental in extending percolation models to TOR.
The study measured Bitcoin's resilience to submarine cable failures using 11 years of network data and found that random failures require 72-92% of cables to fail for significant node disconnection, while targeted attacks are much more effective, and TOR adoption increases resilience by 2-10% under current relay geography.
Bitcoin's design promises resilience through decentralization, yet the physical infrastructure supporting the network creates hidden dependencies. We present the first longitudinal study of Bitcoin's resilience to submarine cable failures, using 11 years of P2P network data (2014--2025) and 68 verified cable fault events. Applying a Buldyrev-style cascade model at country level, we find that Bitcoin's clearnet (non-TOR) critical failure threshold $p_c \approx 0.72$--$0.92$ for random failures, meaning the vast majority of inter-country cables must fail before significant node disconnection. Targeted attacks are an order of magnitude more effective ($p_c = 0.05$--$0.20$). To address the majority of nodes now using TOR with unobservable locations, we develop a 4-layer multiplex model incorporating TOR relay infrastructure. Because relay bandwidth concentrates in well-connected European countries, TOR adoption increases resilience under current relay geography ($Îp_c \approx +0.02$--$+0.10$) rather than introducing hidden fragility. Empirical validation confirms weak physical-layer coupling: 87% of historical cable faults caused less than 5% node impact. We contribute: (1) a multiplex percolation framework for overlay-underlay coupling, including a 4-layer TOR relay model; (2) the first empirical measurement of Bitcoin's physical-layer resilience over a decade; and (3) evidence that TOR adoption amplifies resilience, with distributional bounds quantifying uncertainty under partial observability.