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Single-shot auctions are commonly used as a means to sell goods, for examplewhen selling ad space or allocating radio frequencies, however devisingmechanisms for auctions with multiple bidders and multiple items can becomplicated. It has been shown that neural networks can be used to approximateoptimal mechanisms while satisfying the constraints that an auction bestrategyproof and individually rational. We show that despite such auctionsmaximizing revenue, they do so at the cost of revealing private bidderinformation. While randomness is often used to build in privacy, in thiscontext it comes with complications if done without care. Specifically, it canviolate rationality and feasibility constraints, fundamentally change theincentive structure of the mechanism, and/or harm top-level metrics such asrevenue and social welfare. We propose a method that employs stochasticity toimprove privacy while meeting the requirements for auction mechanisms with onlya modest sacrifice in revenue. We analyze the cost to the auction house thatcomes with introducing varying degrees of privacy in common auction settings.Our results show that despite current neural auctions' ability to approximateoptimal mechanisms, the resulting vulnerability that comes with relying onneural networks must be accounted for.

Neural Auctions Compromise Bidder Information