# Neural Auctions Compromise Bidder Information

## TL;DR

This paper investigates how neural network-based auction mechanisms, while optimizing revenue, can inadvertently leak bidder private information, and proposes a stochastic approach to enhance privacy with minimal revenue loss.

## Contribution

It introduces a method using stochasticity to improve privacy in neural auction mechanisms while maintaining strategyproofness and individual rationality.

## Key findings

- Neural auctions can reveal private bidder information despite revenue optimization.
- Adding controlled randomness can enhance privacy with modest impact on revenue.
- Trade-offs exist between privacy, revenue, and mechanism feasibility in neural auction design.

## Abstract

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.