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Cryptology ePrint Archive

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Incentivizing Geographic Diversity for Decentralized Systems
Marc Roeschlin, Input Output · 2026-04-17 · via Cryptology ePrint Archive

Paper 2026/758

Incentivizing Geographic Diversity for Decentralized Systems

Evangelos Markakis, Input Output, Athens University of Economics and Business,

Raghav Bhaskar, Input Output

Aggelos Kiayias, Input Output, The University of Edinburgh

Abstract

Permissionless Decentralized networks, such as blockchains, are typified by self-determined participation. Unfortunately, this has resulted in lack of geographic diversity in several blockchains due to benefits emanating from network proximity between nodes and the higher availability of computing infrastructure in certain areas. Lack of diversity in the resulting network can make it susceptible to eopolitical events, blockchain or cryptocurrency-adverse law-making, and natural disasters. While there exists a growing body of work in verifiable localization in distributed systems, very little exists on mechanisms promoting geographic diversity in distributed systems. Our work sets out to initiate the study of the incentivization of geographic diversity in permissionless distributed systems. We design a family of mechanisms that incentivize network nodes to truthfully declare and diversify their locations. In particular, we provide a game theoretic analysis to derive the conditions under which truthful location reporting is an equilibrium. The conditions relate the offered rewards (for geo-diversity) and the success probability of the underlying localization protocol to detect falsely claimed locations. Our proposed mechanisms assume an underlying secure node localization protocol based solely on round-trip times (RTT) measurements from participants of the protocol. We initiate a formal model to reason about such localization protocols and identify network topologies that are ideal for resisting location spoofing attempts. We evaluate effectiveness of our incentive mechanisms in different scenarios of node placement and underlying network structure. Our validation is based on two RTT data sets we use to derive maximal spoofing distance and attack success rates that adversarial nodes can achieve when operating alone or in collusion with other nodes.

Note: Full version of the paper presented at Financial Cryptography and data security 2026.

BibTeX

@misc{cryptoeprint:2026/758,
      author = {Marc Roeschlin and Evangelos Markakis and Raghav Bhaskar and Aggelos Kiayias},
      title = {Incentivizing Geographic Diversity for Decentralized Systems},
      howpublished = {Cryptology {ePrint} Archive, Paper 2026/758},
      year = {2026},
      url = {https://eprint.iacr.org/2026/758}
}