  We consider a user releasing her data containing some personal information in
return of a service. We model user's personal information as two correlated
random variables, 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
hypotheses, albeit with different statistics. The user manages data release in
an online fashion such that maximum amount of information is revealed about the
latent useful variable, while the confidence for the sensitive variable is kept
below a predefined level. For the utility, we consider both the probability of
correct detection of the useful variable and the mutual information (MI)
between the useful variable and released data. We formulate both problems as a
Markov decision process (MDP), and numerically solve them by advantage
actor-critic (A2C) deep reinforcement learning (RL).
