Utility/Privacy Trade-off through the lens of Optimal Transport
This addresses privacy preservation in strategic settings like auctions, offering a novel approach to balance utility and information disclosure.
The paper tackles the trade-off between utility and privacy by formalizing it as an optimization problem regularized by information leakage, and it applies this to online repeated auctions, showing that the Sinkhorn loss enables efficient solutions.
Strategic information is valuable either by remaining private (for instance if it is sensitive) or, on the other hand, by being used publicly to increase some utility. These two objectives are antagonistic and leaking this information might be more rewarding than concealing it. Unlike classical solutions that focus on the first point, we consider instead agents that optimize a natural trade-off between both objectives. We formalize this as an optimization problem where the objective mapping is regularized by the amount of information revealed to the adversary (measured as a divergence between the prior and posterior on the private knowledge). Quite surprisingly, when combined with the entropic regularization, the Sinkhorn loss naturally emerges in the optimization objective, making it efficiently solvable. We apply these techniques to preserve some privacy in online repeated auctions.