Privacy Preserving Machine Learning

32 Pages Posted: 6 Nov 2023

Date Written: September 30, 2023


This tutorial provides an introduction into the evolving topic of privacy preserving machine learning. It discusses how to run models on data from potentially multiple data providers without any data provider having to share any non-encrypted data with any other party. In particular, for use cases requiring large amounts of sensitive personal data, and in the context of strict regulations like GDPR in the European Union, this topic is highly relevant. We introduce the concept of (multiparty) homomorphic encryption and demonstrate the approach on two synthetic datasets of personal health information. Furthermore, this tutorial also provides an introduction into the most common (survival) models to predict health outcomes.

Keywords: Life & health, sensitive personal data, cryptography, security, multiparty homomorphic encryption, logistic regression, Cox proportional hazards model, neural network, hazard ratio, relative risk, odds ratio, federated machine learning

JEL Classification: G22, C45, C53, C55, D82, I13

Suggested Citation

Meier, Daniel and Troncoso Pastoriza, Juan R., Privacy Preserving Machine Learning (September 30, 2023). Available at SSRN: or

Daniel Meier (Contact Author)

Swiss Reinsurance Company ( email )

Mythenquai 50/60
P.O. Box
CH-8022 Zurich

Juan R. Troncoso Pastoriza

Tune Insight ( email )

EPFL Innovation Park
Bâtiment C
Lausanne, Vaud 1015

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