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Private and Secure Machine Learning
Antti Honkela · Kana Shimizu · Samuel Kaski

Thu Aug 10 03:30 PM -- 12:30 AM (PDT) @ C4.4
Event URL: https://sites.google.com/view/psml »

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.

Author Information

Antti Honkela (University of Helsinki)
Kana Shimizu (Waseda university)
Samuel Kaski (Aalto University and University of Manchester)

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