  When multiple parties that deal with private data aim for a collaborative
prediction task such as medical image classification, they are often
constrained by data protection regulations and lack of trust among
collaborating parties. If done in a privacy-preserving manner, predictive
analytics can benefit from the collective prediction capability of multiple
parties holding complementary datasets on the same machine learning task. This
paper presents PRICURE, a system that combines complementary strengths of
secure multi-party computation (SMPC) and differential privacy (DP) to enable
privacy-preserving collaborative prediction among multiple model owners. SMPC
enables secret-sharing of private models and client inputs with non-colluding
secure servers to compute predictions without leaking model parameters and
inputs. DP masks true prediction results via noisy aggregation so as to deter a
semi-honest client who may mount membership inference attacks. We evaluate
PRICURE on neural networks across four datasets including benchmark medical
image classification datasets. Our results suggest PRICURE guarantees privacy
for tens of model owners and clients with acceptable accuracy loss. We also
show that DP reduces membership inference attack exposure without hurting
accuracy.
