Title: Protecting Bidder Information in Neural Auctions. Abstract: Single-shot auctions take place all the time, for example when selling ad space or allocating radio frequencies. Devising mechanisms for auctions with many bidders and multiple items can be complicated. It has been shown that neural networks can be used to approximate these mechanisms by satisfying the constraints that an auction be strategyproof and revenue maximizing. 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 and can fundamentally change the incentive structure of the mechanism, and/or harm top-level metrics such as revenue or social welfare. We propose a method based on stochasticity that ensures privacy and meets the requirements for auction mechanisms. Furthermore, we analyze the cost to the auction house in expected revenue that comes with introducing privacy of various degrees.