Differentially Private Online Item Pricing
This addresses privacy concerns in online auctions for buyers, though it is incremental as it builds on existing exponential weights methods.
The authors tackled the problem of revenue maximization in repeated item-pricing auctions while preserving buyer privacy, achieving a sublinear O(√T log T) regret with differential privacy guarantees.
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.