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.

翻译：扩散拍卖是指一种新兴的在线市场范式，拍卖人利用社交网络吸引潜在买家。扩散拍卖存在显著的隐私风险。从拍卖结果中，可以推断出买家隐藏的、可能敏感的偏好。为降低此类风险，我们首次研究了扩散拍卖机制中的差分隐私（DP）。差分隐私是一种成熟的隐私概念，能够保护系统免受推理攻击。在扩散拍卖中实现差分隐私具有挑战性，因为需要精心设计的拍卖规则来激励买家如实报告其社交邻域。我们研究了单物品情形，并设计了两种差分隐私扩散机制（DPDMs）：递归DPDM和分层DPDM。我们证明了这些机制在估值和邻域两个方面都能保证差分隐私、激励相容性和个体理性。随后，我们在真实数据集和合成数据集上对其性能进行了实证比较。