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：传播拍卖是指在线市场的新兴范例,即拍卖人利用社交网络吸引潜在买主。传播拍卖带来了巨大的隐私风险。从拍卖结果中,可以推断出买家的隐蔽和潜在敏感偏好。为了减轻这种风险,我们开始研究扩散拍卖机制中的差别隐私(DP)问题。DP是一个保护系统不受推论攻击的既定隐私概念。在传播拍卖中实现DP是非三重性的,因为需要精心设计的拍卖规则来激励买家真实地报告其邻里。我们研究了单单位案例,设计了两种差别化的私人传播机制:重复式DPDM和分层式DPDM。我们证明这些机制保证不同的隐私、激励兼容性和个人理性,同时保证估值和邻里之间的差异性。然后我们用经验比较它们在真实和合成数据集上的绩效。