发表机构
Rotman School of Management, University of Toronto; NBER（多伦多大学罗特曼管理学院; 美国国家经济研究局）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文研究对抗性指纹识别下的蜜令牌防御设计，提出校准诱饵策略，并分析不同指纹识别成本下的最优信息设计及其持久性。
AI 中文摘要
蜜令牌是一种诱饵凭证或记录，其使用会暴露入侵者，但只有当对手有时将其视为真实时，才能产生防御情报。对手可以选择付费对其进行指纹识别。我将此问题建模为信息设计，随后是接收者的信息获取。在没有指纹识别的情况下，如果防御者安装的陷阱流行率超过对手的参与边界，则会将受攻击的池恰好置于该边界处：即校准诱饵。该后验也最大化了任何具有有限信号结果的固定信息性诊断的总价值。对于完美的隐蔽测试，所得外观设计具有闭式、先验依赖的特征：昂贵的测试保留校准诱饵，中等成本的测试产生欠校准诱饵，而廉价的测试则产生汇集、受保护的分割或弃权（不执行）。陷阱流行率的自由且无成本调整使得非退化分割变得不必要，但足够大的调整成本加上足够高的继承构成可以维持它。最后，沿着外生下降的指纹识别成本路径，较低的成本在欠校准机制内减少了陷阱激活，而不会减少真实入侵。因此，针对新攻击者的评估可能高估可重复使用诱饵的持久性。
英文摘要
A honeytoken, a decoy credential or record whose use reveals an intruder, produces defence intelligence only if an adversary sometimes treats it as genuine. The adversary can instead pay to fingerprint it. I model this problem as information design followed by receiver information acquisition. Without fingerprinting, a defender whose installed trap prevalence exceeds the adversary's participation boundary places the attacked pool exactly at that boundary: calibrated bait. That posterior also maximises the gross value of any fixed informative diagnostic with finitely many signal outcomes. For a perfect covert test, the resulting appearance design has a closed-form, prior-dependent characterisation: expensive testing preserves calibrated bait, intermediate-cost testing produces under-calibrated bait, and cheap testing yields pooling, protected segmentation or abstention. Free and costless adjustment of trap prevalence makes non-degenerate segmentation unnecessary, but a sufficiently large adjustment cost around a sufficiently high inherited composition can sustain it. Finally, along an exogenously declining fingerprinting-cost path, lower cost reduces trap activation without reducing genuine compromise within the under-calibrated regime. Evaluation against fresh attackers can therefore overstate the durability of reusable bait.
