  Internet of things (IoT) devices, such as smart meters, smart speakers and
activity monitors, have become highly popular thanks to the services they
offer. However, in addition to their many benefits, they raise privacy concerns
since they share fine-grained time-series user data with untrusted third
parties. In this work, we consider a user releasing her data containing
personal information in return of a service from an honest-but-curious service
provider (SP). We model user's personal information as two correlated random
variables (r.v.'s), one of them, called the secret variable, is to be kept
private, while the other, called the useful variable, is to be disclosed for
utility. We consider active sequential data release, where at each time step
the user chooses from among a finite set of release mechanisms, each revealing
some information about the user's personal information, i.e., the true values
of the r.v.'s, albeit with different statistics. The user manages data release
in an online fashion such that the maximum amount of information is revealed
about the latent useful variable as quickly as possible, while the confidence
for the sensitive variable is kept below a predefined level. For privacy
measure, we consider both the probability of correctly detecting the true value
of the secret and the mutual information (MI) between the secret and the
released data. We formulate both problems as partially observable Markov
decision processes (POMDPs), and numerically solve them by advantage
actor-critic (A2C) deep reinforcement learning (DRL). We evaluate the
privacy-utility trade-off (PUT) of the proposed policies on both the synthetic
data and smoking activity dataset, and show their validity by testing the
activity detection accuracy of the SP modeled by a long short-term memory
(LSTM) neural network.
