Efficient Commodity Matching for Privacy-Preserving Two-Party Bartering
Fabian Foerg, Susanne Wetzel, Ulrike Meyer · 2017
Current bartering platforms place the burden of finding simultaneously executable quotes on their users. In addition, these bartering platforms do not keep quotes private. To address these shortcomings, this paper introduces a privacy-preserving bartering protocol secure in the semi-honest model. At its core, the novel bartering protocol uses a newly-developed bipartite matching protocol which determines simultaneously executable quotes in an efficient manner. While the new privacy-preserving bipartite matching protocol does not always yield the maximal set of simultaneously executable quotes, it keeps the parties' quotes private at all times. Moreover, our new privacy-preserving bipartite matching protocol is more efficient than existing solutions in that it only requires linear communication in the number of quotes the parties specify.