We propose a general optimization-based framework for computing differentially private M-estimators and a new method for constructing differentially private confidence regions. First, we show that robust statistics can be used in conjunction with noisy gradient descent or noisy Newton methods in order to obtain optimal private estimators with global linear or quadratic convergence, respectively.
- Speaker
- Date
- Thursday 21 Mar 2024, 12:00 - 13:00
- Type
- Seminar
- Room
- ET-14
- Building
- E Building
- Location
- Campus Woudestein
Joint work with Casey Bradshaw and Po-Ling Loh
We establish local and global convergence guarantees, under both local strong convexity and self-concordance, showing that our private estimators converge with high probability to a small neighborhood of the nonprivate M-estimators. Second, we tackle the problem of parametric inference by constructing differentially private estimators of the asymptotic variance of our private M-estimators. This naturally leads to approximate pivotal statistics for constructing confidence regions and conducting hypothesis testing. We demonstrate the effectiveness of a bias correction that leads to enhanced small-sample empirical performance in simulations.
Registration
You can sign up for this seminar by sending an email to eb-secr@ese.eur.nl. The lunch will be provided (vegetarian option included).
Organiser
See also
- More information
Do you want to know more about the event? Contact the secretariat Econometrics at eb-secr@ese.eur.nl.
