Differential Privacy in Statistical Machine Learning

Project description

Machine learning research actively seeks data analyses that enjoy statistical efficiency (accurate predictions) and computational efficiency (scalability to largest datasets). Recently training data privacy has emerged as another dimension of performance for machine learning algorithms. This project explores the frontiers of achieving privacy and utility for machine learning through the formal framework of differential privacy. First proposed in 2006, the framework has become the leading approach to data privacy, has been awarded the 2017 Godel Prize for theoretical computer science, and has been deployed by Apple, Google and the US Census Bureau.

Project team

Leader: Ben Rubinstein

Students: Lingjuan Lyu,Maryam Fanaeepour,Leyla Roohi

Collaborators: Zuhe Zhang (Computing & Information Systems) Sanming Zhou (Mathematics & Statistics) Chris Culnane (Computing & Information Systems) Vanessa Teague (Computing & Information Systems), Francesco Alda (Bochum) Ben Fish (University of Illinois Chicago) Lev Reyzin (University of Illinois Chicago) Justin Bedo (WEHI)

Sponsors: Australian Research Council, National Science Foundation, Transport for NSW

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Computing and Information Systems


Networks and data in society


artificial intelligence; computer security; machine learning