Joseph Geumlek

Joseph Geumlek

Data Scientist at Kaiser Permanente. Interests are in machine learning, with a focus in privacy preserving techniques such as differential privacy. I am also interested in teaching and science communication. Outside of work, I enjoy video games and other such interactive fiction.

Sampling from Distributions under Differential Privacy Notions

Published in UC San Diego (eScholarship), 2020

Recommended citation: Geumlek, J. D. (2020). Sampling from Distributions under Differential Privacy Notions. UC San Diego. ProQuest ID: Geumlek_ucsd_0033D_19410. Merritt ID: ark:/13030/m5p03hfr. Retrieved from https://escholarship.org/uc/item/1dt1m5v1 https://escholarship.org/uc/item/1dt1m5v1

This is my doctoral thesis, synthesizing all of my work completed during my PhD program at UCSD. It explores privacy notions for releasing samples and various methods for achieving them, ranging from privatizing sufficient statistics, changing the posterior update rule, combining soruces of uncertainty through the framework of diffusions, and hiding generating distributions through a convex optimization task.

Download the paper here