Speaker: Yu-Xiang Wang
I will introduce differential privacy in the machine learning setting with an illustrative example. Convince you why it is useful.
I mostly talk about the following two papers:
``Privacy for Free: Posterior Sampling and Stochastic Gradient Monte Carlo''
``Fast Differentially Private Matrix Factorization''
Though if time permits, I will also mention some theoretical understanding from ``Learning with Differential Privacy: Stability, Learnability and the Sufficiency and Necessity of the ERM principle'' and draw connections to the recent buzz of ``Reuseable Holdout'' by Dwork et. al.