Private Evaluation of High-Dimensional Functions
We study differentially private black-box evaluation of arbitrary high-dimensional functions. Given a dataset $S$ and query access to a function $f$---whose sensitivity may be unknown or unbounded---the goal is to privately output an approximation to $f(S)$. The black-box evaluator can only query the value of $f$ over a down neighborhood of $S$ (i.e., those subsets of $S$ that are obtained by removing a bounded number of elements from $S$). We design generic black-box evaluators for both pure and approximate differential privacy through a reduction to high-dimensional private quasi-concave optimization (QCO). Our black-box evaluator enjoys a geometric notion of utility. Consider the vecto...
| Authors | Mohammad Afzali, Hassan Ashtiani, Adam Smith |
|---|---|
| Primary category | stat.ML |
| Published | 17 October 2026 |
| Last updated at source | 17 October 2026 |
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