  We propose a novel problem formulation to address the privacy-utility
tradeoff, specifically when dealing with two distinct user groups characterized
by unique sets of private and utility attributes. Unlike previous studies that
primarily focus on scenarios where all users share identical private and
utility attributes and often rely on auxiliary datasets or manual annotations,
we introduce a collaborative data-sharing mechanism between two user groups
through a trusted third party. This third party uses adversarial privacy
techniques with our proposed data-sharing mechanism to internally sanitize data
for both groups and eliminates the need for manual annotation or auxiliary
datasets. Our methodology ensures that private attributes cannot be accurately
inferred while enabling highly accurate predictions of utility features.
Importantly, even if analysts or adversaries possess auxiliary datasets
containing raw data, they are unable to accurately deduce private features.
Additionally, our data-sharing mechanism is compatible with various existing
adversarially trained privacy techniques. We empirically demonstrate the
effectiveness of our approach using synthetic and real-world datasets,
showcasing its ability to balance the conflicting goals of privacy and utility.
