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To this day, there are still some countries where the exchange of kidneysbetween multiple incompatible patient-donor pairs is restricted by law.Typically, legal regulations in this context are put in place to prohibitcoercion and manipulation in order to prevent a market for organ trade. Yet, incountries where kidney exchange is practiced, existing platforms to facilitatesuch exchanges generally lack sufficient privacy mechanisms. In this paper, wepropose a privacy-preserving protocol for kidney exchange that not onlyaddresses the privacy problem of existing platforms but also is geared to leadthe way in overcoming legal issues in those countries where kidney exchange isstill not practiced. In our approach, we use the concept of secret sharing todistribute the medical data of patients and donors among a set of computingpeers in a privacy-preserving fashion. These computing peers then execute ournew Secure Multi-Party Computation (SMPC) protocol among each other todetermine an optimal set of kidney exchanges. As part of our new protocol, wedevise a privacy-preserving solution to the maximum matching problem on generalgraphs. We have implemented the protocol in the SMPC benchmarking frameworkMP-SPDZ and provide a comprehensive performance evaluation. Furthermore, weanalyze the practicality of our protocol when used in a dynamic setting (wherepatients and donors arrive and depart over time) based on a data set from theUnited Network for Organ Sharing.

Privacy-Preserving Maximum Matching on General Graphs and its Application to Enable Privacy-Preserving Kidney Exchange