  Machine Learning as a Service (MLaaS) is enabling a wide range of smart
applications on end devices. However, such convenience comes with a cost of
privacy because users have to upload their private data to the cloud. This
research aims to provide effective and efficient MLaaS such that the cloud
server learns nothing about user data and the users cannot infer the
proprietary model parameters owned by the server. This work makes the following
contributions. First, it unveils the fundamental performance bottleneck of
existing schemes due to the heavy permutations in computing linear
transformation and the use of communication intensive Garbled Circuits for
nonlinear transformation. Second, it introduces an ultra-fast secure MLaaS
framework, CHEETAH, which features a carefully crafted secret sharing scheme
that runs significantly faster than existing schemes without accuracy loss.
Third, CHEETAH is evaluated on the benchmark of well-known, practical deep
networks such as AlexNet and VGG-16 on the MNIST and ImageNet datasets. The
results demonstrate more than 100x speedup over the fastest GAZELLE (Usenix
Security'18), 2000x speedup over MiniONN (ACM CCS'17) and five orders of
magnitude speedup over CryptoNets (ICML'16). This significant speedup enables a
wide range of practical applications based on privacy-preserved deep neural
networks.
