Privacy-Preserving Fully Online Matching with Deadlines.

In classical secure multi-party computation (SMPC) it is assumed that a fixed and a priori known set of parties wants to securely evaluate a function of their private inputs. This assumption implies that online problems, in which the set of parties that arrive and leave over time are not a priori known, are not covered by the classical setting. Therefore, the notion of online SMPC has been introduced, and a general feasibility result has been proven that shows that any online algorithm can be implemented as a distributed protocol that is secure in this setting [22, 23]. However, so far, no online SMPC protocol that implements a concrete online algorithm has been proposed and evaluated such that the practicality of the constructive proof is an open question. We close this gap and propose the first privacy-preserving online SMPC protocol for the prominent problem of fully online matching with deadlines. In this problem an (a priori unknown) set of parties with their inputs arrive over time and can then be matched with other parties until they leave when their individual deadline is reached. We prove that our protocol is statistically secure in the presence of a semi-honest adversary that controls strictly less than half of the parties present at each point in time. We extensively evaluate the performance of our protocol in three different network settings, various input sizes and different matching conditions, as well as various numbers of parties.