When a group of individuals and organizations wish to compute a stable matching---for example, when medical students are matched to medical residency programs---they often outsource the computation to a trusted arbiter in order to preserve the privacy of participants' preferences. Secure multi-party computation offers the possibility of private matching processes that do not rely on any common trusted third party. However, stable matching algorithms have previously been considered infeasible for execution in a secure multi-party context on non-trivial inputs because they are computationally intensive and involve complex data-dependent memory access patterns.
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Doerner et al. (2016) studied this question.
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