Privacy-Preserving Machine Learning

Privacy-Preserving Machine Learning

Cybersecurity Seminars Online seminar
Monday, 21 June 2021
3 pm - 4 pm (AEST)

Machine learning is now used extensively in many application domains such as pattern recognition, medical diagnosis and credit-risk assessment. Applications of supervised machine learning methods have a common two-phase paradigm: (1) a training phase in which a model is trained from some training data, and (2) a prediction phase in which the trained model is used to predict categories (classification) or continuous values (regression) given some input data. This talk will present a recent work on preserving the privacy of both the training and prediction phases.

The recording of this talk is not available.

About the speaker

Jian Liu
Assistant Professor, Zhejiang University

Dr. Liu have been a ZJU100 Young Professor at Zhejiang University since November 2019. Before that, he was a postdoctoral researcher at UC Berkeley, working with Prof. Dawn Song. He got his PhD in July 2018 from Aalto University, working with Prof. N. Asokan. His research is on Applied Cryptography, Distributed Systems, Blockchains and Machine Learning. He is interested in building real-world systems that are provably secure, easy to use and inexpensive to deploy.

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