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：扩散拍卖指一种新兴的在线市场范式，其中拍卖者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：通过拍卖结果，可能推断出买家隐藏且具有潜在敏感性的偏好。为降低此类风险，我们首次在扩散拍卖机制中引入差分隐私研究。差分隐私是一种成熟的隐私保护概念，可防止系统遭受推理攻击。在扩散拍卖中实现差分隐私极具挑战性，因为需要精心设计的拍卖规则来激励买家如实报告其邻域信息。我们针对单物品情形展开研究，设计了两种满足差分隐私的扩散机制：递归型与分层型。我们证明这两种机制在估值和邻域两个维度均能保证差分隐私、激励相容性和个体理性。最后，我们在真实与合成数据集上对其性能进行了实证比较。