  Deep Learning as a Service (DLaaS) stands as a promising solution for
cloud-based inference applications. In this setting, the cloud has a
pre-learned model whereas the user has samples on which she wants to run the
model. The biggest concern with DLaaS is user privacy if the input samples are
sensitive data. We provide here an efficient privacy-preserving system by
employing high-end technologies such as Fully Homomorphic Encryption (FHE),
Convolutional Neural Networks (CNNs) and Graphics Processing Units (GPUs). FHE,
with its widely-known feature of computing on encrypted data, empowers a wide
range of privacy-concerned applications. This comes at high cost as it requires
enormous computing power. In this paper, we show how to accelerate the
performance of running CNNs on encrypted data with GPUs. We evaluated two CNNs
to classify homomorphically the MNIST and CIFAR-10 datasets. Our solution
achieved a sufficient security level (> 80 bit) and reasonable classification
accuracy (99%) and (77.55%) for MNIST and CIFAR-10, respectively. In terms of
latency, we could classify an image in 5.16 seconds and 304.43 seconds for
MNIST and CIFAR-10, respectively. Our system can also classify a batch of
images (> 8,000) without extra overhead.
