Abstract
This manuscript is the outcome of the work performed during the Homomorphic Encryption Standardization Strategic Planning Meeting 2020, held at Microsoft Research premises in Redmond, on February 5–6, 2020. This document describes how complex analysis tasks on sensitive data held by mutually untrusted data providers can be enabled by means of Multiparty Homomorphic Encryption (MHE), both in distributed and centralized environments. We showcase this approach in the medical sector, as it is a paradigmatic example where privacy is paramount and data sharing is needed. We show that MHE can be used to efficiently streamline and facilitate data discovery (Raisaro JL, Troncoso-Pastoriza JR, Misbach M, Sousa JS, Pradervand S, Missiaglia E, Michielin O, Ford B, Hubaux JP, IEEE/ACM Trans Comput Biol Bioinf 16(4):1328–1341, https://doi.org/10.1109/TCBB.2018.2854776, 2019; Froelicher D, Egger P, Sousa JS, Raisaro JL, Huang Z, Mouchet C, Ford B, Hubaux JP, UnLynx: a decentralized system for privacy-conscious data sharing. In: Proceedings on privacy enhancing technologies, vol 4, no. EPFL-CONF-229308, pp 152–170, 2017) and complex analysis (Froelicher D, Troncoso-Pastoriza JR, Sousa JS, Hubaux JP, IEEE Trans Inf Forensics Secur, 2020; Froelicher D, Troncoso-Pastoriza J, Pyrgelis A, Sav S, Sa Sousa J, Bossuat J-P, Hubaux J-P, Scalable privacy-preserving distributed learning. In: Proceedings on privacy enhancing technologies, vol 2, 2021; Aloufi A, Hu P, Wong HWH, Chow SSM, Blindfolded evaluation of random forests with multi-key homomorphic encryption. In: IEEE Transactions on Dependable and Secure Computing (TDSC), September 2019; Sinem S, Pyrgelis A, Troncoso-Pastoriza JR, Froelicher D, Bossuat J-P, Sa Sousa J, Hubaux JP, POSEIDON: privacy-preserving federated neural network learning. Accepted at NDSS 2021), e.g., training and evaluation of machine learning models, in environments in which the data are particularly sensitive, thus enabling secure collaborations in domains in which data sharing is usually difficult or even impossible with traditional technologies.
This is a preview of subscription content, access via your institution.
Buying options
References
J. V. Selby, A. C. Beal, and L. Frank. “The patient-centered outcomes research institute (PCORI) national priorities for research and initial research agenda,” JAMA, vol. 307, no. 15, pp. 1583–1584, 2012.
Swiss Academies of Arts and Sciences. “Swiss Personalized Health Network,” http://www.samw.ch/en/Projects/SPHN.html, last Accessed: July 23, 2019.
The Global Alliance for Genomics and Health. “A federated ecosystem for sharing genomic, clinical data,” Science, vol. 352, no. 6291, pp. 1278–1280, 2016.
“All of us research program,” https://allofus.nih.gov/, last accessed: July 23, 2019.
EU Parliament. “The EU General Data Protection Regulation (GDPR),” http://www.eugdpr.org/, last Accessed: July 23, 2019.
U.S. Department of Health & Human Services. “The health insurance portability and accountability act (HIPAA),” https://www.hhs.gov/hipaa/index.html, last Accessed: July 23, 2019.
OECD (2019), Health at a Glance 2019: OECD Indicators, OECD Publishing, Paris, https://doi.org/10.1787/4dd50c09-en
Gross domestic R&D expenditure on health (health GERD) as a % of gross domestic product (GDP). World Health Organization. Global Observatory on Health R&D. January 2020. Available online: https://www.who.int/research-observatory/indicators/gerd_gdp/
Federated Data Systems: Balancing Innovation and Trust in the Use of Sensitive Data, World Economic Forum, July 2019. Available online: https://www.weforum.org/whitepapers/federated-data-systems-balancing-innovation-and-trust-in-the-use-of-sensitive-data/
Value in Healthcare: Mobilizing cooperation for health system transformation, World Economic Forum, February 2018. Available online: https://www.weforum.org/reports/value-in-healthcare-mobilizing-cooperation-for-health-system-transformation/
Christian Mouchet, Juan Troncoso-Pastoriza, Jean-Philippe Bossuat, Jean-Pierre Hubaux. “Multiparty Homomorhic Encryption from Ring-Learning-with-Errors,” in Proceedings on Privacy Enhancing Technologies, vol. 4, pp. 291–311, 2021.
James Scheibner, Jean Louis Raisaro, Juan Ramón Troncoso-Pastoriza, Marcello Ienca, Jacques Fellay, Effy Vayena, Jean-Pierre Hubaux. “Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis”. Journal of Medical Internet Research, vol. 23, No. 2. February 2021, https://doi.org/10.2196/25120
J. L. Raisaro, J. R. Troncoso-Pastoriza, M. Misbach, J. S. Sousa, S. Pradervand, Edoardo Missiaglia, Olivier Michielin, Bryan Ford and Jean-Pierre Hubaux, “MedCo: Enabling Secure and Privacy-Preserving Exploration of Distributed Clinical and Genomic Data,” IEEE/ACM Transactions on computational biology and bioinformatics. vol. 16, no. 4, pp. 1328–1341, 1 July-Aug. 2019. https://doi.org/10.1109/TCBB.2018.2854776
D. Froelicher, J.R. Troncoso-Pastoriza, J.S. Sousa, and J.P. Hubaux. “Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets,” IEEE Transactions on Information Forensics and Security, vol. 15, pp. 3035–3050, 2020. https://doi.org/10.1109/TIFS.2020.2976612.
David Froelicher, Juan Troncoso-Pastoriza, Apostolos Pyrgelis, Sinem Sav, Joao Sa Sousa, Jean-Philippe Bossuat, Jean-Pierre Hubaux. “Scalable Privacy-Preserving Distributed Learning,” in Proceedings on Privacy Enhancing Technologies, vol. 2, pp. 323–347, 2021.
Sav, Sinem, Apostolos Pyrgelis, Juan R. Troncoso-Pastoriza, David Froelicher, Jean-Philippe Bossuat, Joao Sa Sousa, and Jean-Pierre Hubaux. “POSEIDON: Privacy-Preserving Federated Neural Network Learning.” NDSS 2021.
Asma Aloufi, Peizhao Hu, Harry W.H. Wong, and Sherman S.M. Chow. “Blindfolded Evaluation of Random Forests with Multi-Key Homomorphic Encryption,” in IEEE Transactions on Dependable and Secure Computing (TDSC). Sept 2019.
Ran Gilad-Bachrach, Nathan Dowlin, Kim Laine, Kristin Lauter, Michael Naehrig, and John Wernsing. “Cryptonets: Applying neural networks to encrypted data with high throughput and accuracy,” in International Conference on Machine Learning, pp. 201–210. 2016.
Ehsan Hesamifard, Hassan Takabi, and Mehdi Ghasemi. “CryptoDL: Towards Deep Learning over Encrypted Data.” In Annual Computer Security Applications Conference (ACSAC 2016), Los Angeles, California, USA, vol. 11. 2016.
Asma Aloufi, Peizhao Hu, Yongsoo Song, and Kristin Lauter. “Computing Blindfolded on Data Homomorphically Encrypted under Multiple Keys: An Extended Survey.” https://arxiv.org/abs/2007.09270
Homomorphic Encryption Standardization Group. https://homomorphicEncryption.org
S.N. Murphy, G. Weber, M. Mendis, V. Gainer, H.C. Chueh, S. Churchill, and I. Kohane. “Serving the enterprise and beyond with informatics for integrating biology and the bedside (i2b2),” Journal of the American Medical Informatics Association, vol.17, no.2, pp.124–130, 2010
B. D. Athey, M. Braxenthaler, M. Haas, and Y. Guo. “tranSMART: an open source and community-driven informatics and data sharing platform for clinical and translational research,” AMIA Summits on Translational Science Proceedings, vol. 2013, p. 6, 2013.
D. Froelicher, P. Egger, J. S. Sousa, J. L. Raisaro, Z. Huang, C. Mouchet, B. Ford, and J.-P. Hubaux. “UnLynx: A decentralized system for privacy-conscious data sharing,” in Proceedings on Privacy Enhancing Technologies, vol. 4, pp. 152–170, 2017.
MedCo – Legal perspective. Available online at https://medco.epfl.ch
C. A. Neff. “Verifiable mixing (shuffling) of ElGamal pairs.” VHTi Technical Document, VoteHere, Inc, 2003.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Troncoso-Pastoriza, J., Froelicher, D., Hu, P., Aloufi, A., Hubaux, JP. (2021). Privacy-Preserving Data Sharing and Computation Across Multiple Data Providers with Homomorphic Encryption. In: Lauter, K., Dai, W., Laine, K. (eds) Protecting Privacy through Homomorphic Encryption. Springer, Cham. https://doi.org/10.1007/978-3-030-77287-1_3
Download citation
DOI: https://doi.org/10.1007/978-3-030-77287-1_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-77286-4
Online ISBN: 978-3-030-77287-1
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)