Differentially Private Diffusion Auction: The Single-unit Case

TL;DR

This paper introduces differentially private mechanisms for diffusion auctions in online social networks, ensuring privacy and truthful reporting while maintaining auction incentives, with empirical performance evaluation.

Contribution

It proposes two novel DP mechanisms for diffusion auctions that guarantee privacy, incentive compatibility, and individual rationality in the single-unit case.

Findings

Both mechanisms guarantee differential privacy and incentive compatibility.

Empirical results show effective privacy preservation with competitive auction performance.

The layered DPDM outperforms recursive DPDM in certain scenarios.

Abstract

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.