Jean-Pierre Hubaux

Also known as Jean‐Pierre Hubaux

Jean-Pierre Hubaux is a computer scientist at EPFL whose work on security and privacy led to ways of training neural networks on data that stays encrypted. After an engineering degree in Milan and 10 years at Alcatel, he joined EPFL in 1990 and became a full professor in 1996. His 2011 paper Quantifying Location Privacy won the IEEE Security and Privacy Test-of-Time Award in 2021, and POSEIDON, a system he coauthored for NDSS 2021, trained neural networks across several parties with multiparty lattice-based cryptography. In 2021, he co-founded Tune Insight, a start-up for privacy-preserving machine learning. Today he is the academic director of EPFL's Center for Digital Trust, which EPFL unveiled in December 2017.

Now at: EPFL
Born: 1957
Works: 628
Citations: 36,024
h-index: 88
Badges: Founder
Bridge
Loyal

Years link to their sources. Publication numbers from OpenAlex, as of 22 October 2026.

Timeline

1990–1996

From Alcatel to EPFL

Born in Liège, Belgium, he grows up in Italy, earns an engineering degree at the Polytechnic University of Milan and spends 10 years at the French telecom company Alcatel, mainly on switching systems. He joins EPFL as an associate professor in 1990, leads what becomes its Laboratory for Data Security, and becomes a full professor in 1996. He also holds visiting positions at the Thomas J. Watson Research Center and the University of California, Berkeley.
2007–2010

Wireless security book and fellowships

In 2007, he publishes Security and Cooperation in Wireless Networks with Levente Buttyán at Cambridge University Press. He becomes an IEEE Fellow in 2009 for contributions to wireless security and an ACM Fellow in 2010.
2008–2019

Swiss communications commissioner

From 2008 to 2019, he is one of the seven commissioners of the Swiss Federal Commission for Communications.
December 2017–November 2018

Founding the Center for Digital Trust

EPFL unveils the Center for Digital Trust (C4DT) in December 2017, with Hubaux as its founding academic director. At its founding General Assembly in November 2018, partners including the ICRC, the Lausanne University Hospital and Swisscom confirm their commitment, and more than 30 EPFL labs take part in its work on data protection, encryption, machine learning and blockchain.
April 2018–December 2021

Privacy-preserving neural network training

From April 2018 to December 2021, he leads the Data Protection in Personalized Health project funded by the ETH Council. With Sinem Sav and colleagues, he develops POSEIDON, accepted at NDSS in 2021, which trains neural networks across N parties with multiparty lattice-based cryptography while keeping the training data, the model and the evaluation data confidential. The system trains a 3-layer network on MNIST, split among 10 parties, in less than 2 hours.
2021

A Test-of-Time Award

Quantifying Location Privacy, his paper with Reza Shokri, George Theodorakopoulos and Jean-Yves Le Boudec first presented at IEEE S&P in 2011, wins the IEEE Security and Privacy Test-of-Time Award in 2021. The paper offers a formal framework for measuring the privacy risks of sharing location data.
2021–2023

Co-founding Tune Insight

In 2021, he co-founds Tune Insight with Juan R. Troncoso Pastoriza, Frederic Pont and Romain Bouyé, a start-up for confidential collaborative analytics and privacy-preserving machine learning. By 2022, Tune Insight has industrialized FAMHE, a federated analytics system based on multiparty homomorphic encryption, and installed it at several customers. In September 2023, the company raises a $3.4 million seed round led by 14Peaks Capital.
As of October 2026

Academic director of C4DT

He is the academic director of EPFL's Center for Digital Trust, with expertise in privacy-preserving federated learning and data protection in personalized health. In 2026, he serves on GESDA's Anticipation Committee for digital security.