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31.07.2023 9:00 am - 03.08.2023 4:00 pm
The IACR Privacy-Preserving Machine Learning (PPML) summer school will soon be organized at the IDEAS NCBR headquarters.

This is an offer addressed to students of master’s studies, doctoral students, postdocs from Poland and abroad, primarily in the fields of cryptography, mathematics, and theoretical computer science.

Privacy in machine learning is an important and exciting research topic that focuses on how to reap the benefits of machine learning techniques while keeping training data and learned models private.

In the second edition of the IACR Privacy-Preserving Machine Learning (PPML) school, lecturers with theoretical and practical experience will discuss a wide range of issues related to this topic, such as secure computing, differential privacy or federated learning.

Event agenda:

09:2009:30Stefan Dziembowski – opening remarks 
09:3011:00Rafael Dowsley – A ML crash course for the schoolNishanth Chandran – MPC & ML 2Emiliano de Cristofaro – Synthetic data
(online talk)
11:3013:00Nishanth Chandran – MPC and ML 1Peter Kairouz – Federated Learning & Differential Privacy 1Celia Kherfallah – TFHE in action using concrete-MLPeter Kairouz – Federated Learning & Differential Privacy 2
14:3016:00Yuriy Polyakov – FHE for MLYang Zhang – Model extraction & membership inferenceYang Zhang – Backdoor attacks and other fun attacksPeter Kairouz – Federated Learning & Differential Privacy 3 (finishing at 15:30)
16:3018:00Yuriy Polyakov – FHE in action using Open FHERafael Dowsley – PPML, just not for Deep Neural NetsDr. Grażyna Żebrowska (IDEAS NCBR board member) –Research and development at IDEAS NCBR
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