Protecting data
for personalized health

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P4 (Predictive, Preventive, Personalized and Participatory) medicine is called to revolutionize healthcare by providing better diagnoses and targeted preventive and therapeutic measures. However, to accelerate its adoption and maximize its potential, clinical and research data on large numbers of individuals must be efficiently shared between all stakeholders. The privacy risks stemming from disclosing medical data raise serious concerns, and have become a barrier that can hold back the advances in P4 medicine if effective privacy-preserving technologies are not adopted to enable privacy-conscious medical data sharing. The evolution of the regulation towards further guarantees (e.g., HIPAA in USA and the GDPR in EU) reflects this urgent need.

The combination of data sharing with recent advances in the field of *omics and, in particular, in high-throughput sequencing technology, leads to an explosive growth in the amounts of available data; this big data scale can usually not be handled with current hospital computing facilities, hence the need for elastic computing resources that can cope with huge amounts of data in a secure and privacy-aware infrastructure, supporting data processing and sharing.

At EPFL, we are working on different aspects of health data privacy and security with strong collaboration with medical doctors, bioinformaticians, geneticists, and other specialists. We focus, in particular, in the following main research directions:

  • Data protection: we make use of decentralized cryptographic protocols to prevent data leakage during computation
  • Privacy: we quantify the risk of inference attacks and propose techniques to minimize it.

EPFL/LDS Researchers

Prof. Jean-Pierre Hubaux
Dr. Juan Ramon Troncoso-Pastoriza
Dr. Apostolos Pyrgelis
David Froelicher
Mickaël Misbach
Christian Mouchet
Joao Sa

Academic Partners

Prof. Erman AydayCase Western University
Prof. Jacques Fellay, EPFL and CHUV
Prof. Bryan Ford, EPFL
Dr. Mathias Humbert, Swiss Data Science Center
Prof. Ari Juels, Cornell Tech
Prof. Bradley Malin, Vanderbilt University
Prof. Shawn Murphy , Harvard University / Partners Healthcare
Dr. Jean Louis RaisaroEPFL and CHUV
Prof. Amalio Telenti, Scripps Institute.
Prof. XiaoFeng Wang, Indiana University at Bloomington

Journal/Magazine Articles


Conference/Workshop Paper




Technical Reports


  • S. Sav, A. Pyrgelis, J.R. Troncoso-Pastoriza, D. Froelicher, J.-P. Bossuat, J.S. Sousa and J.-P. Hubaux. System and method for privacy-preserving distributed training of neural network models on distributed datasets. Provisional patent application has been filed PCT/EP2020/074031, 2021.
  • D. Froelicher, J.R. Troncoso-Pastoriza, A. Pyrgelis, S. Sav, J.S. Sousa, J.-P. Bossuat and J.-P. Hubaux. System and method for privacy-preserving distributed training of machine learning models on distributed datasets. Provisional patent application has been filed PCT/EP2020/062810, 2021.

  • B. Ford, J-P. Hubaux, p. Egger, J.L. Raisaro, Z. Huang. System and method for providing a collective decentralized authority for sharing sensitive data. Provisional patent application has been filed PCT/EP2016/079649, 2015.
  • Jean-Pierre Hubaux, Erman Ayday, Jean Louis Raisaro, Urs Hengartner, Adam Molyneaux, Zhenyu Xu, Jurgi Camblong, and Pierre Hutter Privacy-Preserving Processing of Raw Genomic Data Provisional patent application has been filed, No. 13172607.7, June 2013.
  • Erman Ayday, Mathias Humbert, Jacques Fellay, Paul J. McLaren, Jacques Rougemont, Jean Louis Raisaro, Amalio Telenti and Jean-Pierre Hubaux Genomic Privacy Protection Provisional patent application has been filed, No 12184372.6 and 61/700,897, September 2012.