论文标题
通过线性编程优化仿射最大化拍卖：零日漏洞的收入最大化机制设计的应用
Optimizing Affine Maximizer Auctions via Linear Programming: an Application to Revenue Maximizing Mechanism Design for Zero-Day Exploits Markets
论文作者
论文摘要
在仿射最大化器拍卖（AMA）中进行优化是收入最大化机制设计的有效方法。 AMA机制是防策略的，并且是个人合理的（如果代理人对结果的估值是无负的）。每个AMA机制的特征都以参数列表。通过关注AMA机制，我们将机制设计变成了价值优化问题，我们只需要调整参数即可。我们提出了一种基于线性编程的启发式，以在AMA家族中优化。我们将技术应用于最大化机制设计的收入，用于零日的利用市场。我们表明，由于零日的利用市场的性质，如果只有两个代理商（一名罪犯和一名辩护人），那么我们的技术通常会产生一种几乎最佳的机制：该机制的预期收入接近最佳策略 - 防止策略 - 防护机制和个性化机制（不一定是AMA机制）。
Optimizing within the affine maximizer auctions (AMA) is an effective approach for revenue maximizing mechanism design. The AMA mechanisms are strategy-proof and individually rational (if the agents' valuations for the outcomes are nonnegative). Every AMA mechanism is characterized by a list of parameters. By focusing on the AMA mechanisms, we turn mechanism design into a value optimization problem, where we only need to adjust the parameters. We propose a linear programming based heuristic for optimizing within the AMA family. We apply our technique to revenue maximizing mechanism design for zero-day exploit markets. We show that due to the nature of the zero-day exploit markets, if there are only two agents (one offender and one defender), then our technique generally produces a near optimal mechanism: the mechanism's expected revenue is close to the optimal revenue achieved by the optimal strategy-proof and individually rational mechanism (not necessarily an AMA mechanism).
