Privacy-Preserving Machine Learning

Data privacy is a huge concern and often prevents ML and AI project from flourishing.
  In this talk we will introduce you to federated learning and homomorphic encryption. After we’ve covered the theoretical aspects we will see how they can be used in practice.
  We conclude with an outlook on the future of these technologies.
Romeo Kienzler

Chief Data Scientist at the IBM Center for Open Source Data and AI Technologies (CODAIT). He works as Associate Professor for AI at the Swiss University of Applied Sciences Berne and Adjunct Professor for Information Security at the Swiss University of Applied Sciences Northwestern Switzerland (FHNW). His current research focus is on cloud-scale machine learning and deep learning using open source technologies including TensorFlow, Keras, and the Apache Spark stack.   Recently he joined the Linux Foundation AI as lead for the Trusted AI technical workgroup with focus on Deep Learning Adversarial Robustness, Fairness and Explainability.
  • Date: Jul 27, 10:00 (US Pacific Time)
  • Fee: Free
  • Available Seats: 0 (max 200)
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