Seminar

Q-Math Seminar

Quantum-inspired solutions for privacy leaks in machine learning

Speaker:  Alejandro Pozas-Kerstjens (ICMAT)
Date:  Friday, 11 November 2022 - 13:00
Place:  Room 2.2.D08, Universidad Carlos III de Madrid

Abstract:

Vast amounts of data are routinely processed in machine learning pipelines, every time covering more aspects of our interactions with the world. However, the quest for performance is leaving other important aspects, such as privacy, on the side. For example, when the models processing the data are made public, is the safety of the data used for training it guaranteed? This is a question of utmost importance especially when processing sensitive data such as medical records.


In this talk, I will argue and practically illustrate that insights in quantum information, concretely coming from the tensor network representations of quantum many-body states, can help in devising better privacy-preserving machine learning algorithms. In the first part, I will show that standard neural networks are vulnerable to a type of privacy leak that involves global properties of the data used for training, thus being a priori resistant to standard protection mechanisms. In the second, I will show that tensor networks, when used as machine learning architectures, are invulnerable to this vulnerability. The proof of the resilience is based on the existence of canonical forms for tensor networks. Given the growing expertise in training tensor networks and the recent interest in tensor-based reformulations of popular machine learning architectures, these results imply that one may not have to be forced to make a choice between accuracy in prediction and ensuring the privacy of the information processed when using machine learning on sensitive data.