Statistical Approximating Distributions Under Differential Privacy
By Yue Wang, Daniel Kifer, Jaewoo Lee, Vishesh Karwa. 2018
23 citations recorded by the source corpus.
Statistics computed from data are viewed as random variables. When they are used for tasks like hypothesis testing and confidence intervals, their true finite sample distributions are often replaced by approximating distributions that are easier to work with (for example, the Gaussian, which results from using approximations justified by the Central Limit Theorem). When data are perturbed by differential privacy, the approximating distributions also need to be modified. Prior work provided various competing methods for creating such approximating distributions with little formal justification beyond the fact that they worked well empirically. In this paper, we study the question of how to generate statistical approximating distributions for differentially private statistics, provide finite sample guarantees for the quality of the approximations.
Subfields: Artificial Intelligence
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In Artificial Intelligence
- Gautam Kamath
- Jonathan Ullman
- Adam D. Smith
- Thomas Steinke
- Jayadev Acharya
- Clément Louis Canonne
- Audra McMillan
- Mark Bun
Citations in the corpus
- The structure of optimal private tests for simple hypotheses (2018, background): “[75] focuses on generating statistical approximating distributions for differentially private statistics, which they apply to hypothesis testing problems.”