Achieving Prior-Aligned Utility-Privacy Trade-Off for Data Sharing

Summary

This paper proposes a unified utility-privacy modeling framework for data sharing that selects only the most informative features satisfying explicit privacy constraints, instead of sharing all attributes or fixed subsets. The trade-off is formulated as a constrained optimization problem, with privacy constraints encoded as weighted penalty terms, and utility gain and privacy loss are estimated via normalized mutual information (NMI). The optimization uses a relaxed dual formulation Lagrangian to characterize the utility-privacy trade-off frontier.