617 research outputs found
BAYHENN: Combining Bayesian Deep Learning and Homomorphic Encryption for Secure DNN Inference
Recently, deep learning as a service (DLaaS) has emerged as a promising way
to facilitate the employment of deep neural networks (DNNs) for various
purposes. However, using DLaaS also causes potential privacy leakage from both
clients and cloud servers. This privacy issue has fueled the research interests
on the privacy-preserving inference of DNN models in the cloud service. In this
paper, we present a practical solution named BAYHENN for secure DNN inference.
It can protect both the client's privacy and server's privacy at the same time.
The key strategy of our solution is to combine homomorphic encryption and
Bayesian neural networks. Specifically, we use homomorphic encryption to
protect a client's raw data and use Bayesian neural networks to protect the DNN
weights in a cloud server. To verify the effectiveness of our solution, we
conduct experiments on MNIST and a real-life clinical dataset. Our solution
achieves consistent latency decreases on both tasks. In particular, our method
can outperform the best existing method (GAZELLE) by about 5x, in terms of
end-to-end latency.Comment: accepted by IJCAI 2019; camera read
Application of Bayesian Model Selection in Fluorescence Correlation Spectroscopy (FCS) to WNT3EGFP Secretion and Diffusion in Zebrafish Embryos
Ph.DDOCTOR OF PHILOSOPH
G2C: A Generator-to-Classifier Framework Integrating Multi-Stained Visual Cues for Pathological Glomerulus Classification
Pathological glomerulus classification plays a key role in the diagnosis of
nephropathy. As the difference between different subcategories is subtle,
doctors often refer to slides from different staining methods to make
decisions. However, creating correspondence across various stains is
labor-intensive, bringing major difficulties in collecting data and training a
vision-based algorithm to assist nephropathy diagnosis. This paper provides an
alternative solution for integrating multi-stained visual cues for glomerulus
classification. Our approach, named generator-to-classifier (G2C), is a
two-stage framework. Given an input image from a specified stain, several
generators are first applied to estimate its appearances in other staining
methods, and a classifier follows to combine visual cues from different stains
for prediction (whether it is pathological, or which type of pathology it has).
We optimize these two stages in a joint manner. To provide a reasonable
initialization, we pre-train the generators in an unlabeled reference set under
an unpaired image-to-image translation task, and then fine-tune them together
with the classifier. We conduct experiments on a glomerulus type classification
dataset collected by ourselves (there are no publicly available datasets for
this purpose). Although joint optimization slightly harms the authenticity of
the generated patches, it boosts classification performance, suggesting more
effective visual cues are extracted in an automatic way. We also transfer our
model to a public dataset for breast cancer classification, and outperform the
state-of-the-arts significantly.Comment: Accepted by AAAI 201
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