Privacy-Preserving Machine Learning: Machine learning on data you cannot see

Privacy guarantees are one of the most crucial requirements when it comes to analyse sensitive information. However, data anonymisation techniques alone do not always provide complete privacy protection; moreover Machine Learning (ML) models could also be exploited to leak sensitive data when attacked and no counter-measure is put in place.

Privacy-preserving machine learning (PPML) methods hold the promise to overcome all those issues, allowing to train machine learning models with full privacy guarantees.

This workshop will be mainly organised in two parts. In the first part, we will explore one example of ML model exploitation (i.e. inference attack ) to reconstruct original data from a trained model, and we will then see how differential privacy can help us protecting the privacy of our model, with minimum disruption to the original pipeline. In the second part of the workshop, we will examine a more complicated ML scenario to train Deep learning networks on encrypted data, with specialised distributed federated learning strategies.