  Minimizing privacy leakage while ensuring data utility is a critical problem
to data holders in a privacy-preserving data publishing task. Most prior
research concerns only with one type of data and resorts to a single obscuring
method, \eg, obfuscation or generalization, to achieve a privacy-utility
tradeoff, which is inadequate for protecting real-life heterogeneous data and
is hard to defend ever-growing machine learning based inference attacks. This
work takes a pilot study on privacy-preserving data publishing when both
generalization and obfuscation operations are employed for heterogeneous data
protection. To this end, we first propose novel measures for privacy and
utility quantification and formulate the hybrid privacy-preserving data
obscuring problem to account for the joint effect of generalization and
obfuscation. We then design a novel hybrid protection mechanism called
HyObscure, to cross-iteratively optimize the generalization and obfuscation
operations for maximum privacy protection under a certain utility guarantee.
The convergence of the iterative process and the privacy leakage bound of
HyObscure are also provided in theory. Extensive experiments demonstrate that
HyObscure significantly outperforms a variety of state-of-the-art baseline
methods when facing various inference attacks under different scenarios.
HyObscure also scales linearly to the data size and behaves robustly with
varying key parameters.
