Mikael Skoglund

On Privacy Mechanism Design Under a Bounded $\alpha$-Lift Leakage Constraint with $\chi^2$-Utility

We study the design of a statistical privacy mechanism under a bounded $\alpha$-lift leakage constraint. An agent observes useful data $Y$, which is arbitrarily correlated with private data $X$, and releases $U$. The goal is to maximize Pearson $\chi^2$-information $\chi^2(Y;U)$ while satisfying the point-wise privacy...