University of Cambridge > Talks.cam > Statistics > Towards multi-purpose locally differentially-private synthetic data release via plug-in estimation

  Browse A-Z

Log in

University Account External (via Google)

🔐 Log In

Information on

Subscribing to talksdetails Finding a talkdetails Adding a talkdetails Disseminating talksdetails Help and Documentationdetails

ℹī¸ Information

Towards multi-purpose locally differentially-private synthetic data release via plug-in estimation

Download to your calendar using vCal

If you have a question about this talk, please contact Po-Ling Loh .

We develop plug-in estimators for locally differentially private semi-parametric estimation via spline wavelets. The approach leads to optimal rates of convergence for a large class of estimation problems that are characterized by (differentiable) functionals $\Lambda(f)$ of the true data generating density $f$. The crucial feature of the locally private data $Z_1,\dots, Z_n$ we generate is that it does not depend on the particular functional $\Lambda$ (or the unknown density $f$) the analyst wants to estimate. Hence, the synthetic data can be generated and stored a priori and can subsequently be used by any number of analysts to estimate many vastly different functionals of interest at the provably optimal rate. In principle, this removes a long standing practical limitation in statistics of differential privacy, namely, that optimal privacy mechanisms need to be tailored towards the specific estimation problem at hand.

This talk is co-hosted by the Informed-AI Hub.

This talk is part of the Statistics series.

This talk is included in these lists:

Note that ex-directory lists are not shown.

Other lists

Center for Mathematical Sciences, MR3 MR14 - note venue change music

Other talks

Coming soon Online Causal Inference Seminar Security Cooperation within Government and between Governments Cambridge AI in Medicine Seminar - April 2026 Tea and Coffee Break Fairy Tales
 

Š 2006-2025 Talks.cam, University of Cambridge. Contact Us | Help and Documentation | Privacy and Publicity