  Deep Learning algorithms have recently become the de-facto paradigm for
various prediction problems, which include many privacy-preserving applications
like online medical image analysis. Presumably, the privacy of data in a deep
learning system is a serious concern. There have been several efforts to
analyze and exploit the information leakages from deep learning architectures
to compromise data privacy. In this paper, however, we attempt to provide an
evaluation strategy for such information leakages through deep neural network
architectures by considering a case study on Convolutional Neural Network (CNN)
based image classifier. The approach takes the aid of low-level hardware
information, provided by Hardware Performance Counters (HPCs), during the
execution of a CNN classifier and a simple hypothesis testing in order to
produce an alarm if there exists any information leakage on the actual input.
