  Computing an AUC as a performance measure to compare the quality of different
machine learning models is one of the final steps of many research projects.
Many of these methods are trained on privacy-sensitive data and there are
several different approaches like $\epsilon$-differential privacy, federated
machine learning and methods based on cryptographic approaches if the datasets
cannot be shared or evaluated jointly at one place. In this setting, it can
also be a problem to compute the global AUC, since the labels might also
contain privacy-sensitive information. There have been approaches based on
$\epsilon$-differential privacy to deal with this problem, but to the best of
our knowledge, no exact privacy preserving solution has been introduced. In
this paper, we propose an MPC-based framework, called privacy preserving AUC
(ppAUC), with novel methods for comparing two secret-shared values, selecting
between two secret-shared values, converting the modulus and performing
division to compute the exact AUC as one could obtain on the pooled original
test samples. We employ ppAUC in the computation of the exact area under
precision-recall curve and receiver operating characteristic curve even for
ties between prediction confidence values. To prove the correctness of ppAUC,
we apply it to evaluate a model trained to predict acute myeloid leukemia
therapy response and we also assess its scalability via experiments on
synthetic data. The experiments show that we efficiently compute exactly the
same AUC with both evaluation metrics in a privacy preserving manner as one can
obtain on the pooled test samples in the plaintext domain. Our solution
provides security against semi-honest corruption of at most one of the servers
performing the secure computation.
