Joseph Lam (Magdeburg)
The consequences of local differential privacy constraints on goodness-of-fit testing are considered, i.e. the statistical problem assessing whether sample points are generated from a fixed density or not. The observations are hidden and replaced by a stochastic transformation satisfying the local differential privacy constraint. The focus will be on the lower bound, leading to the minimax optimality of our result over Besov balls.
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