Integrating Homomorphic Encryption and Trusted Execution Technology for
  Autonomous and Confidential Model Refining in Cloud
Pinglan Liu Wensheng Zhang
With the popularity of cloud computing and machine learning, it has been a
trend to outsource machine learning processes (including model training and
model-based inference) to cloud. By the outsourcing, other than utilizing the
extensive and scalable resource offered by the cloud service provider, it will
also be attractive to users if the cloud servers can manage the machine
learning processes autonomously on behalf of the users. Such a feature will be
especially salient when the machine learning is expected to be a long-term
continuous process and the users are not always available to participate. Due
to security and privacy concerns, it is also desired that the autonomous
learning preserves the confidentiality of users' data and models involved.
Hence, in this paper, we aim to design a scheme that enables autonomous and
confidential model refining in cloud. Homomorphic encryption and trusted
execution environment technology can protect confidentiality for autonomous
computation, but each of them has their limitations respectively and they are
complementary to each other. Therefore, we further propose to integrate these
two techniques in the design of the model refining scheme. Through
implementation and experiments, we evaluate the feasibility of our proposed
scheme. The results indicate that, with our proposed scheme the cloud server
can autonomously refine an encrypted model with newly provided encrypted
training data to continuously improve its accuracy. Though the efficiency is
still significantly lower than the baseline scheme that refines plaintext-model
with plaintext-data, we expect that it can be improved by fully utilizing the
higher level of parallelism and the computational power of GPU at the cloud
server.