Differentially Private Online Item Pricing. (arXiv:2305.11362v1 [cs.GT])

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 …

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