This work addresses the problem of revenue maximization in a repeated,
unlimited supply item-pricing auction while preserving buyer privacy. We
present a novel algorithm that provides differential privacy with respect to
the buyer's input pair: item selection and bid. Notably, our algorithm is the
first to offer a sublinear $O(\sqrt{T}\log{T})$ regret with a privacy
guarantee. Our method is based on an exponential weights meta-algorithm, and we
mitigate the issue of discontinuities in revenue functions via small random
perturbations. As a result of its structural similarity to the exponential
mechanism, our method inherently secures differential privacy. We also extend
our algorithm to accommodate scenarios where buyers strategically bid over
successive rounds. The inherent differential privacy allows us to adapt our
algorithm with minimal modification to ensure a sublinear regret in this
setting.

本次研究讨论如何在无限供应的物品定价拍卖中最大化收益并保护买家隐私，提出了一种新算法，提供了不同隐私保证的指数权重元算法，对于收益函数的间断问题进行了缓解，其结构类似于指数机制，适用于买家分阶段进行竞标的情况，具有次线性的遗憾率。