微分私人扩散拍卖：单单位情形
Differentially Private Diffusion Auction: The Single-unit Case
摘要：
扩散拍卖是一种新兴的在线市场模式，拍卖人利用社交网络吸引潜在买家。扩散拍卖带来了重大的隐私风险。从拍卖结果中，可以推断出买家隐藏的、潜在敏感的偏好。为了降低这种风险，我们开始研究扩散拍卖机制中的差分隐私（DP）。DP是一个完善的隐私概念，可以保护系统免受推理攻击。在扩散拍卖中实现DP是不平凡的，因为设计良好的拍卖规则需要激励买家如实报告他们的邻居。我们研究了单单元的情况下，设计了两个差分私有扩散机制（DPDM）：递归DPDM和分层DPDM。我们证明，这些机制保证差分隐私，激励相容性和个人理性的估值和邻里。然后，我们根据经验比较了它们在真实和合成数据集上的性能。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
