Skip to yearly menu bar Skip to main content


Workshop

Private and Secure Machine Learning

Antti Honkela · Kana Shimizu · Samuel Kaski

C4.4

Thu 10 Aug, 3:30 p.m. PDT

There are two complementary approaches to private and secure machine learning: differential privacy can guarantee privacy of the subjects of the training data with respect to the output of a differentially private learning algorithm, while cryptographic approaches can guarantee secure operation of the learning process in a potentially distributed environment. The aim of this workshop is to bring together researchers interested in private and secure machine learning, to stimulate interactions to advance either perspective or to combine them.

Live content is unavailable. Log in and register to view live content

Timezone: America/Los_Angeles

Schedule

Log in and register to view live content