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Call for Contributions
Workshop on Private and Secure Machine Learning 2017
at ICML 2017, Sydney, Australia
11th August 2017
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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 discuss the most recent results in this very rapidly progressing and exciting field, and to bring together researchers from both private and secure machine learning, to stimulate interactions to advance either perspective or to combine them.
We invite submissions of abstracts for talks and posters at PSML2017 on all aspects of private and secure ML. The abstracts should be 300-600 words long. The program committee will select several submitted abstracts for 20-minute talks. PSML2017 will consider submissions of both published and unpublished work. Accepted abstracts will be published on the workshop website.
We look forward for submissions that are novel, exciting and that appeal to the wider community. For more details see:
Please submit your contributions at
https://www.easychair.org/conferences/?conf=psml2017