Physics solutions for machine learning privacy leaks

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Pozas Kerstjens, Alejandro and Hernández Santana, Senaida and Pareja Monturiol, José Ramón and Castrillón López, Marco and Scarpa, Giannicola and Gonzalez Guillen, Carlos E. and Pérez García, David (2022) Physics solutions for machine learning privacy leaks. (Unpublished)

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Abstract

Machine learning systems are becoming more and more ubiquitous in increasingly complex areas, including cutting-edge scientific research. The opposite is also true: the interest in better understanding the inner workings of machine learning systems motivates their analysis under the lens of different scientific disciplines. Physics is particularly successful in this, due to its ability to describe complex dynamical systems. While explanations of phenomena in machine learning based physics are increasingly present, examples of direct application of notions akin to physics in order to improve machine learning systems are more scarce. Here we provide one such pplication in the problem of developing algorithms that preserve the privacy of the manipulated data, which is especially important in tasks such as the processing of medical records. We develop well-defined conditions to guarantee robustness to specific types of privacy leaks, and rigorously prove that such conditions are satisfied by tensor-network architectures. These are inspired by the efficient representation of quantum many-body systems, and have shown to compete and even surpass traditional machine learning architectures in certain cases. Given the growing expertise in training tensornetwork 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.


Item Type:Article
Uncontrolled Keywords:Machine learning; Complex dynamical systems; Tensor-network architectures; Criptography
Subjects:Sciences > Physics > Mathematical physics
Sciences > Computer science > Computer security
Sciences > Mathematics > Operations research
ID Code:73160
Deposited On:28 Jun 2022 18:17
Last Modified:29 Jun 2022 06:56

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