  In this paper, we tackle the problem of constructing a differentially private
synopsis for the classification analyses. Several the state-of-the-art methods
follow the structure of existing classification algorithms and are all
iterative, which is suboptimal due to the locally optimal choices and the
over-divided privacy budget among many sequentially composed steps. Instead, we
propose a new approach, PrivPfC, a new differentially private method for
releasing data for classification. The key idea is to privately select an
optimal partition of the underlying dataset using the given privacy budget in
one step. Given one dataset and the privacy budget, PrivPfC constructs a pool
of candidate grids where the number of cells of each grid is under a data-aware
and privacy-budget-aware threshold. After that, PrivPfC selects an optimal grid
via the exponential mechanism by using a novel quality function which minimizes
the expected number of misclassified records on which a histogram classifier is
constructed using the published grid. Finally, PrivPfC injects noise into each
cell of the selected grid and releases the noisy grid as the private synopsis
of the data. If the size of the candidate grid pool is larger than the
processing capability threshold set by the data curator, we add a step in the
beginning of PrivPfC to prune the set of attributes privately. We introduce a
modified $\chi^2$ quality function with low sensitivity and use it to evaluate
an attribute's relevance to the classification label variable. Through
extensive experiments on real datasets, we demonstrate PrivPfC's superiority
over the state-of-the-art methods.
