Diffusion auction refers to an emerging paradigm of online marketplace where
an auctioneer utilises a social network to attract potential buyers. Diffusion
auction poses significant privacy risks. From the auction outcome, it is
possible to infer hidden, and potentially sensitive, preferences of buyers. To
mitigate such risks, we initiate the study of differential privacy (DP) in
diffusion auction mechanisms. DP is a well-established notion of privacy that
protects a system against inference attacks. Achieving DP in diffusion auctions
is non-trivial as the well-designed auction rules are required to incentivise
the buyers to truthfully report their neighbourhood. We study the single-unit
case and design two differentially private diffusion mechanisms (DPDMs):
recursive DPDM and layered DPDM. We prove that these mechanisms guarantee
differential privacy, incentive compatibility and individual rationality for
both valuations and neighbourhood. We then empirically compare their
performance on real and synthetic datasets