基于VDF的随机性信标经济安全：模型、阈值与设计指南
Economic Security of VDF-Based Randomness Beacons: Models, Thresholds, and Design Guidelines
AI总结：
本文研究基于VDF的随机性信标经济安全，提出模型与阈值分析，揭示合理对手的攻击决策，并提出经济安全参数设计指南。
摘要：
基于可验证延迟函数（VDF）的随机性信标在区块链和分布式系统中日益受到关注，承诺公开可验证的延迟和抗偏移性。然而，现有分析将对手视为纯粹的密码学实体，忽略了实际攻击者具有经济动机。VDF可能在序列上安全，但若理性对手通过购买更快的硬件和利用如MEV机会等奖励激增获利，则仍可能被攻破。本文开发了一个正式框架，将攻击者建模为理性代理，考虑硬件加速、运营成本和随机奖励，将攻击决策视为最优停止问题，并证明最优行为具有单调阈值结构。这导致了延迟参数与对手成本和奖励分布之间的紧密必要和充分条件。我们扩展分析至磨矿、选择中止和多对手竞争，展示了每种如何放大有效奖励并增加所需延迟。利用现实的云成本、硬件基准和MEV数据，我们表明许多提议的VDF延迟，大约几秒，但在合理条件下经济不安全。我们最后提出可部署的指南，并引入经济安全延迟参数（ESDPs）以支持实际系统中的原则性参数选择。
英文摘要：
Randomness beacons based on Verifiable Delay Functions (VDFs) are increasingly proposed for blockchains and distributed systems, promising publicly verifiable delay and bias resistance. Existing analyses, however, treat adversaries purely as cryptographic entities and overlook that real attackers are economically motivated. A VDF may be sequentially secure, yet still vulnerable if a rational adversary can profit by purchasing faster hardware and exploiting reward spikes such as MEV opportunities. We develop a formal framework for economic security of VDF-based randomness beacons. Modeling the attacker as a rational agent facing hardware speedup, operating costs, and stochastic rewards, we cast the attack decision as an optimal-stopping problem and prove that optimal behavior has a monotone threshold structure. This yields tight necessary and sufficient conditions relating delay parameters to adversarial cost and reward distributions. We extend the analysis to grinding, selective abort, and multi-adversary competition, demonstrating how each amplifies effective rewards and increases required delays. Using realistic cloud costs, hardware benchmarks, and MEV data, we show that many proposed VDF delays, on the order of a few seconds, are economically insecure under plausible conditions. We conclude with deployable guidelines and introduce Economically Secure Delay Parameters (ESDPs) to support principled parameter selection in practical systems.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
