Single-shot auctions are commonly used as a means to sell goods, for example when selling ad space or allocating radio frequencies, however devising mechanisms for auctions with multiple bidders and multiple items can be complicated. It has been shown that neural networks can be used to approximate optimal mechanisms while satisfying the constraints that an auction be strategyproof and individually rational. We show that despite such auctions maximizing revenue, they do so at the cost of revealing private bidder information. While randomness is often used to build in privacy, in this context it comes with complications if done without care. Specifically, it can violate rationality and feasibility constraints, fundamentally change the incentive structure of the mechanism, and/or harm top-level metrics such as revenue and social welfare. We propose a method that employs stochasticity to improve privacy while meeting the requirements for auction mechanisms with only a modest sacrifice in revenue. We analyze the cost to the auction house that comes with introducing varying degrees of privacy in common auction settings. Our results show that despite current neural auctions' ability to approximate optimal mechanisms, the resulting vulnerability that comes with relying on neural networks must be accounted for.

翻译：单轮拍卖常用于商品销售，例如广告位出售或无线电频率分配，但设计面向多竞标者、多物品的拍卖机制往往十分复杂。已有研究表明，神经网络可在满足策略证明性和个体理性约束的条件下逼近最优拍卖机制。我们发现，尽管此类拍卖能最大化收益，但代价是泄露竞标者的隐私信息。虽然随机性常被用于构建隐私保护机制，但在该场景中若操作不当将引发复杂问题：具体而言，可能违反理性与可行性约束、从根本上改变机制的激励结构，并损害收益与社会福利等顶层指标。我们提出一种引入随机性以提升隐私保护能力的方法，该方法在满足拍卖机制约束的前提下，仅以适度收益损失为代价。我们分析了在常见拍卖场景中引入不同隐私保护程度对拍卖方的成本影响。结果表明，尽管当前神经拍卖具备逼近最优机制的能力，但依赖神经网络所带来的潜在风险必须予以重视。