Privacy-Preserving Intrusion Detection using Convolutional Neural
  Networks
Martin Kodys Zhongmin Dai Vrizlynn L. L. Thing
Privacy-preserving analytics is designed to protect valuable assets. A common
service provision involves the input data from the client and the model on the
analyst's side. The importance of the privacy preservation is fuelled by legal
obligations and intellectual property concerns. We explore the use case of a
model owner providing an analytic service on customer's private data. No
information about the data shall be revealed to the analyst and no information
about the model shall be leaked to the customer. Current methods involve costs:
accuracy deterioration and computational complexity. The complexity, in turn,
results in a longer processing time, increased requirement on computing
resources, and involves data communication between the client and the server.
In order to deploy such service architecture, we need to evaluate the optimal
setting that fits the constraints. And that is what this paper addresses. In
this work, we enhance an attack detection system based on Convolutional Neural
Networks with privacy-preserving technology based on PriMIA framework that is
initially designed for medical data.