all InfoSec news
Differentially Private Online Item Pricing. (arXiv:2305.11362v1 [cs.GT])
cs.CR updates on arXiv.org arxiv.org
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 …
addresses algorithm auction differential privacy input log novel offer pricing privacy private problem respect revenue supply work