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Privacy-Preserving Machine Learning is an important and exciting research subject that investigates how to benefit from machine learning techniques while preserving the privacy of training data and learned models. At the 2nd IACR Privacy-Preserving Machine Learning (PPML) school, lecturers with theoretical and applied backgrounds will cover a broad spectrum of subjects related to this topic, such as secure computation, differential privacy, federated learning, and practical attacks.
The school is organized by Stefan Dziembowski and the IDEAS NCBR Institute. It is partly supported by the International Association for Cryptologic Research (IACR).
The school will follow the format of the 1st PPML school that was organized by Carsten Baum and Bernardo David in Copenhagen in 2022.