Fisher Information Under Local Differential Privacy
We develop data processing inequalities that describe how Fisher information from statistical samples can scale with the privacy parameter $\varepsilon $ under local differential privacy constraints. These bounds are valid under general conditions on the distribution of the score of the statistical model, and they elucidate under which conditions the dependence on $\varepsilon $ is linear, quadratic, or exponential.