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Background: The kidney exchange problem (KEP) addresses the matching ofpatients in need for a replacement organ with compatible living donors. Ideallymany medical institutions should participate in a matching program to increasethe chance for successful matches. However, to fulfill legal requirementscurrent systems use complicated policy-based data protection mechanisms thateffectively exclude smaller medical facilities to participate. Employing securemulti-party computation (MPC) techniques provides a technical way to satisfydata protection requirements for highly sensitive personal health informationwhile simultaneously reducing the regulatory burdens.  Results: We have designed, implemented, and benchmarked SPIKE, a secureMPC-based privacy-preserving KEP which computes a solution by finding matchingdonor-recipient pairs in a graph structure. SPIKE matches 40 pairs in cycles oflength 2 in less than 4 minutes and outperforms the previous state-of-the-artprotocol by a factor of 400x in runtime while providing medically more robustsolutions.  Conclusions: We show how to solve the KEP in a robust and privacy-preservingmanner achieving practical performance. The usage of MPC techniques fulfillsmany data protection requirements on a technical level, allowing smaller healthcare providers to directly participate in a kidney exchange with reduced legalprocesses.

SPIKE: Secure and Private Investigation of the Kidney Exchange problem