  We propose Falcon, an end-to-end 3-party protocol for efficient private
training and inference of large machine learning models. Falcon presents four
main advantages - (i) It is highly expressive with support for high capacity
networks such as VGG16 (ii) it supports batch normalization which is important
for training complex networks such as AlexNet (iii) Falcon guarantees security
with abort against malicious adversaries, assuming an honest majority (iv)
Lastly, Falcon presents new theoretical insights for protocol design that make
it highly efficient and allow it to outperform existing secure deep learning
solutions. Compared to prior art for private inference, we are about 8x faster
than SecureNN (PETS'19) on average and comparable to ABY3 (CCS'18). We are
about 16-200x more communication efficient than either of these. For private
training, we are about 6x faster than SecureNN, 4.4x faster than ABY3 and about
2-60x more communication efficient. Our experiments in the WAN setting show
that over large networks and datasets, compute operations dominate the overall
latency of MPC, as opposed to the communication.
