  An increasing number of sensors on mobile, Internet of things (IoT), and
wearable devices generate time-series measurements of physical activities.
Though access to the sensory data is critical to the success of many beneficial
applications such as health monitoring or activity recognition, a wide range of
potentially sensitive information about the individuals can also be discovered
through access to sensory data and this cannot easily be protected using
traditional privacy approaches.
  In this paper, we propose a privacy-preserving sensing framework for managing
access to time-series data in order to provide utility while protecting
individuals' privacy. We introduce Replacement AutoEncoder, a novel algorithm
which learns how to transform discriminative features of data that correspond
to sensitive inferences, into some features that have been more observed in
non-sensitive inferences, to protect users' privacy. This efficiency is
achieved by defining a user-customized objective function for deep
autoencoders. Our replacement method will not only eliminate the possibility of
recognizing sensitive inferences, it also eliminates the possibility of
detecting the occurrence of them. That is the main weakness of other approaches
such as filtering or randomization. We evaluate the efficacy of the algorithm
with an activity recognition task in a multi-sensing environment using
extensive experiments on three benchmark datasets. We show that it can retain
the recognition accuracy of state-of-the-art techniques while simultaneously
preserving the privacy of sensitive information. Finally, we utilize the GANs
for detecting the occurrence of replacement, after releasing data, and show
that this can be done only if the adversarial network is trained on the users'
original data.
