  Over the last decade, proliferation of various online platforms and their
increasing adoption by billions of users have heightened the privacy risk of a
user enormously. In fact, security researchers have shown that sparse microdata
containing information about online activities of a user although anonymous,
can still be used to disclose the identity of the user by cross-referencing the
data with other data sources. To preserve the privacy of a user, in existing
works several methods (k-anonymity, l-diversity, differential privacy) are
proposed that ensure a dataset which is meant to share or publish bears small
identity disclosure risk. However, the majority of these methods modify the
data in isolation, without considering their utility in subsequent knowledge
discovery tasks, which makes these datasets less informative. In this work, we
consider labeled data that are generally used for classification, and propose
two methods for feature selection considering two goals: first, on the reduced
feature set the data has small disclosure risk, and second, the utility of the
data is preserved for performing a classification task. Experimental results on
various real-world datasets show that the method is effective and useful in
practice.
