software . 2020

Code and data for INSPECTRE: Privately Estimating the Unseen

Acharya, Jayadev; Kamath, Gautam; Sun, Ziteng; Zhang, Huanyu;
Open Access
  • Published: 08 Apr 2020
  • Publisher: Zenodo
Abstract
<p>Code and data for the published article.</p> <p>We develop differentially private methods for estimating various distributional properties. Given a sample from a discrete distribution p, some functional f, and accuracy and privacy parameters alpha and epsilon, the goal is to estimate f(p) up to accuracy alpha, while maintaining epsilon-differential privacy of the sample. We prove almost-tight bounds on the sample size required for this problem for several functionals of interest, including support size, support coverage, and entropy. We show that the cost of privacy is negligible in a variety of settings, both theoretically and experimentally. Our methods are...
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Zenodo
Software . 2020
Provider: Datacite
Zenodo
Software . 2020
Provider: Datacite
Zenodo
Software . 2020
Provider: Zenodo
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