Differentially Private Selection using Smooth Sensitivity
This addresses the challenge of maintaining privacy while improving accuracy in data science queries, though it is incremental as it builds on existing sensitivity concepts.
The paper tackled the problem of excessive noise in differentially private selection mechanisms by proposing the Smooth Noisy Max (SNM) mechanism, which uses smooth sensitivity to achieve tighter error bounds and demonstrated higher accuracy than state-of-the-art methods in applications like percentile selection and decision trees.
Differentially private selection mechanisms offer strong privacy guarantees for queries aiming to identify the top-scoring element r from a finite set R, based on a dataset-dependent utility function. While selection queries are fundamental in data science, few mechanisms effectively ensure their privacy. Furthermore, most approaches rely on global sensitivity to achieve differential privacy (DP), which can introduce excessive noise and impair downstream inferences. To address this limitation, we propose the Smooth Noisy Max (SNM) mechanism, which leverages smooth sensitivity to yield provably tighter (upper bounds on) expected errors compared to global sensitivity-based methods. Empirical results demonstrate that SNM is more accurate than state-of-the-art differentially private selection methods in three applications: percentile selection, greedy decision trees, and random forests.