Thomas Steinke
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- Adam M. Smithonce
- Adam Sealfononce
- Aleksandra Slavkoviconce
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| title | conference | date |
|---|---|---|
| Privately Estimating Black-Box Statistics | Trust in Decentralized Systems 2026 | 2026-03-16 |
Provides a concrete scheme that privately estimates arbitrary black‑box functions by evaluating F on overlapping subsets and aggregating with the shifted… | ||
| Algorithms with More Granular Differential Privacy Guarantees | ITCS 2023 | 2023-01-10 |
Defines per-attribute differential privacy and gives concrete algorithms that trade per-person epsilon for much smaller per-attribute epsilon0. Uses Gaussian… | ||
| PANEL: Statistical Theory, Privacy and Data Analysis | Privacy and the Science of Data Analysis 2019 | 2019-04-08 |
Panel argues that differential privacy acts as both a constraint and an enabler: it enforces data hygiene, gives formal generalization guarantees, and opens… | ||
| Open Problem Session | Privacy and the Science of Data Analysis 2019 | 2019-04-08 |
Nine eight-minute open problems present concrete conjectures, partial results and explicit gaps: a private VC equivalence conjecture linking private uniform… | ||
| Smooth Sensitivity, Revisited | Privacy and the Science of Data Analysis 2019 | 2019-04-08 |
A differentially private Gaussian mean estimator that matches the nonprivate variance up to a low‑order privacy cost, achieved by applying smooth sensitivity… | ||
| Preventing Overfitting in Adaptive Data Analysis via Stability | Optimization, Statistics and Uncertainty | 2017-11-28 |
Claims that adaptive answers can have distribution-dependent accuracy that scales with each query's variance rather than worst-case range, and gives algorithms… | ||
| nothing matches | ||