Two and Three-Party Digital Goods Auctions: Scalable Privacy Analysis
Patrick Ah-Fat, Michael Huth

TL;DR
This paper develops scalable privacy analysis techniques for digital goods auctions involving two and three parties, providing formulas to quantify information leaks and supporting evidence for extension to more parties.
Contribution
It introduces closed-form formulas for privacy leakage in small-party auctions, enabling scalable analysis of bid confidentiality in digital goods auctions.
Findings
Derived closed-form formulas for posterior min-entropy in 2- and 3-party auctions.
Quantified information leaks for large input spaces.
Experimental evidence suggests extension to any number of parties.
Abstract
A digital goods auction is a type of auction where potential buyers bid the maximal price that they are willing to pay for a certain item, which a seller can produce at a negligible cost and in unlimited quantity. To maximise her benefits, the aim for the seller is to find the optimal sales price, which every buyer whose bid is not lower will pay. For fairness and privacy purposes, buyers may be concerned about protecting the confidentiality of their bids. Secure Multi-Party Computation is a domain of Cryptography that would allow the seller to compute the optimal sales price while guaranteeing that the bids remain secret. Paradoxically, as a function of the buyers' bids, the sales price inevitably reveals some private information. Generic frameworks and entropy-based techniques based on Quantitative Information Flow have been developed in order to quantify and restrict those leakages.…
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Taxonomy
TopicsPrivacy-Preserving Technologies in Data · Cryptography and Data Security · Blockchain Technology Applications and Security
