Differentially private spectrum auction with approximate revenue maximization

Abstract

Dynamic spectrum redistribution—–under which spectrum owners lease out under-utilized spectrum to users for finan-cial gain—–is an effective way to improve spectrum utiliza-tion. Auction is a natural way to incentivize spectrum own-ers to share their idle resources. In recent years, a number of strategy-proof auction mechanisms have been proposed to stimulate bidders to truthfully reveal their valuations. However, it has been shown that truthfulness is not a nec-essary condition for revenue maximization. Furthermore, in most existing spectrum auction mechanisms, bidders may infer the valuations—–which are private information—–of the other bidders from the auction outcome. In this paper, we propose a Differentially privatE spectrum auction mech-anism with Approximate Revenue maximization (DEAR). We theoretically prove that DEAR achieves approximate truthfulness, privacy preservation, and approximate revenue maximization. Our extensive evaluations show that DEAR achieves good performance in terms of both revenue and privacy preservation