  Many modern databases include personal and sensitive correlated data, such as
private information on users connected together in a social network, and
measurements of physical activity of single subjects across time. However,
differential privacy, the current gold standard in data privacy, does not
adequately address privacy issues in this kind of data.
  This work looks at a recent generalization of differential privacy, called
Pufferfish, that can be used to address privacy in correlated data. The main
challenge in applying Pufferfish is a lack of suitable mechanisms. We provide
the first mechanism -- the Wasserstein Mechanism -- which applies to any
general Pufferfish framework. Since this mechanism may be computationally
inefficient, we provide an additional mechanism that applies to some practical
cases such as physical activity measurements across time, and is
computationally efficient. Our experimental evaluations indicate that this
mechanism provides privacy and utility for synthetic as well as real data in
two separate domains.
