MediSC: towards secure and lightweight deep learning as a medical diagnostic service

The striking progress of deep learning paves the way towards intelligent and quality medical diagnostic services. Enterprises deploy such services via the neural network (NN) inference, yet confronted with rising privacy concerns of the medical data being diagnosed and the pre-trained NN models. We propose, a system framework that enables enterprises to offer secure medical diagnostic service to their customers via an execution of NN inference in the ciphertext domain. ensures the privacy of both parties with cryptographic guarantees. At the heart, we present an efficient and communication-optimized secure inference protocol that purely relies on the lightweight secret sharing techniques and can well cope with the commonly-used linear and non-linear NN layers. Compared to the garbled circuits based solutions, the latency and communication of are 24 × lower and 868 × less for the secure ReLU, and 20 × lower and 314 × less for the secure Max-pool. We evaluate on two benchmark and four real-world medical datasets, and comprehensively compare it with prior arts. The results demonstrate the promising performance of, which is much more bandwidth-efficient compared to prior works.