  In the big data era, more and more cloud-based data-driven applications are
developed that leverage individual data to provide certain valuable services
(the utilities). On the other hand, since the same set of individual data could
be utilized to infer the individual's certain sensitive information, it creates
new channels to snoop the individual's privacy. Hence it is of great importance
to develop techniques that enable the data owners to release privatized data,
that can still be utilized for certain premised intended purpose. Existing data
releasing approaches, however, are either privacy-emphasized (no consideration
on utility) or utility-driven (no guarantees on privacy). In this work, we
propose a two-step perturbation-based utility-aware privacy-preserving data
releasing framework. First, certain predefined privacy and utility problems are
learned from the public domain data (background knowledge). Later, our approach
leverages the learned knowledge to precisely perturb the data owners' data into
privatized data that can be successfully utilized for certain intended purpose
(learning to succeed), without jeopardizing certain predefined privacy
(training to fail). Extensive experiments have been conducted on Human Activity
Recognition, Census Income and Bank Marketing datasets to demonstrate the
effectiveness and practicality of our framework.
