  Ensuring the privacy of training data is a growing concern since many machine
learning models are trained on confidential and potentially sensitive data.
Much attention has been devoted to methods for protecting individual privacy
during analyses of large datasets. However in many settings, global properties
of the dataset may also be sensitive (e.g., mortality rate in a hospital rather
than presence of a particular patient in the dataset). In this work, we depart
from individual privacy to initiate the study of attribute privacy, where a
data owner is concerned about revealing sensitive properties of a whole dataset
during analysis. We propose definitions to capture \emph{attribute privacy} in
two relevant cases where global attributes may need to be protected: (1)
properties of a specific dataset and (2) parameters of the underlying
distribution from which dataset is sampled. We also provide two efficient
mechanisms and one inefficient mechanism that satisfy attribute privacy for
these settings. We base our results on a novel use of the Pufferfish framework
to account for correlations across attributes in the data, thus addressing "the
challenging problem of developing Pufferfish instantiations and algorithms for
general aggregate secrets" that was left open by \cite{kifer2014pufferfish}.
