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.

我们提出了一种新的问题形式来处理隐私与效用之间的权衡，特别是针对由具有独特私人和效用属性集的两个不同用户组组成的情况。我们通过一个可信第三方引入了两个用户组之间的协作数据共享机制，该机制使用对抗隐私技术对数据进行内部清洁，消除了手动注释或辅助数据集的需要。我们的方法确保了无法准确推断私人属性，同时实现了高度准确的效用特征预测。此外，我们的数据共享机制与各种现有的对抗隐私技术兼容，并通过使用合成和真实世界数据集进行实证展示，展示了其平衡隐私和效用这一互相冲突目标的能力。

通过协调优化隐私与效用的权衡来满足群体利益