Differentially Private Diffusion Auction: The Single-unit Case

Fengjuan Jia (University of Electronic Science and Technology of China), Mengxiao Zhang (University of Electronic Science and Technology of China), Jiamou Liu (The University of Auckland), Bakh Khoussainov (University of Electronic Science and Technology of China)

Abstract

Diffusion auction refers to an emerging paradigm where an auctioneer utilises a social network to attract potential buyers. We consider the risks of disclosing sensitive preferences of buyers from the published auction outcome and initiate the study of differential privacy in diffusion auction. We study the single-unit case and design two differentially private diffusion mechanisms (DPDMs): recursive DPDM and layered DPDM. We prove their incentive and privacy properties, and then empirically compare their performance on real and synthetic datasets.