November 16, 2022

Data Protection in Personalized Health (DPPH)

1st Call, PM / PH Research

Short Summary

DPPH addresses the main privacy, security, scalability, and ethical challenges of data sharing for enabling effective P4 medicine, by defining an optimal balance between usability, scalability and data protection. The main result of the project will be a platform composed of software packages that seamlessly enable clinical and genomic data sharing and exploitation across a federation of medical institutions, hospitals and research laboratories across Switzerland in a scalable, secure, responsible and privacy-conscious way, and that integrates widespread cohort exploration tools and analysis frameworks.

Goals

DPPH seeks to address the main privacy, security, scalability, and ethical challenges of data sharing for enabling effective P4 medicine, by defining an optimal balance between usability, scalability and data protection, and deploying an appropriate set of computing tools to make it happen. This main goal materializes in the following outcomes that the project expects to deliver: (i) A holistic requirements analysis of the medical data sharing ecosystem, from the standpoint of legal, ethical and medical stakeholders, (ii) a scalable scientific computing infrastructure, building on top of Swiss Data Science Center’s (SDSC) data science framework, (iii) software-based solutions for accountable and privacy-preserving data sharing featuring trust distribution across a federation of sites with no single points of failure, (iv) a quantitative analysis of inference risks, and countermeasures for addressing them when releasing aggregated results on patient data, and (v) a comprehensive ethical analysis of distributed platforms for medical data sharing.

Significance

DPPH is meant to combine knowledge from the data science, computer science, ethics, medicine and genomics communities to effectively tackle the challenges currently thwarting data sharing for P4 medicine. The software platforms and prototypes produced by the project are meant to be enablers that effectively combine secure and privacy-conscious data access and processing with large-scale collaborative medical research, addressing the main technological barriers holding up advances personalized medicine. The privacy and ethical frameworks enable an in-depth analysis and evaluation of current and future systems, allowing for future-proofness of the used platforms under current and upcoming strict regulatory frameworks. By establishing liaisons with other PHRT/SPHN projects, DPPH seeks to cover the Swiss national level, targeting prototypes at a national scale, and by leveraging on already established connections with the Global Alliance for Genomics and Health (GA4GH) and its Software Security Group, DPPH also guarantees international relevance and consistency.

Background

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 new 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.

Publications

Pham, I. Dacosta, E. Losiouk, J. Stephan, K. Huguenin, and J-P. Hubaux, HideMyApp: Hiding the Presence of Sensitive Apps on Android, in 28th USENIX Security Symposium, Santa Clara, California, USA, 2019
Raisaro, JR. Troncoso-Pastoriza, S. Pradervand, M. Cuendet, M. Misbach, J. Sa, F. Marino, N. Freundler, N. Rosat, D. Cavin, A. Leichtle, J. Fellay, O. Michielin, and J-P. Hubaux, SPHN/PHRT-MedCo in Action: Empowering the Swiss Molecular Tumor Board with Privacy-Preserving and Real-Time Patient Discovery, Studies in Health Technology and Informatics, 2020 Jun 1;270:1161-2
Raisaro, JR. Troncoso-Pastoriza, S. Pradervand, M. Cuendet, M. Misbach, J. Sa, F. Marino, N. Freundler, N. Rosat, D. Cavin, A. Leichtle, J. Fellay, O. Michielin, and J-P. Hubaux, SPHN/PHRT-MedCo in Action: Empowering the Swiss Molecular Tumor Board with Privacy-Preserving and Real-Time Patient Discovery, Studies in Health Technology and Informatics, 2020 Jun 1;270:1161-2
Scheibner, M. Ienca, S. Kechagia, JR. Troncoso-Pastoriza, JL. Raisaro, J-P. Hubaux, and E. Vayena, Data Protection and Ethics Requirements for Multisite Research with Health Data: A Comparative Examination of Legislative Governance Frameworks and the Role of Data Protection Technologies, Journal of Law and the Biosciences, 2020
Carpov, N. Gama, M. Georgieva, and JR. Troncoso-Pastoriza, Privacy-preserving Semi-parallel Logistic Regression Training with Fully Homomorphic Encryption, BMC Medical Genomics 13, 88, 2020
Grishin, JL. Raisaro, JR. Troncoso-Pastoriza, K. Obbad, K. Quinn, M. Misbach, J. Gollhardt, J. Sa Sousa, J. Fellay, GM. Church, and J-P. Hubaux, Citizen-centered, Auditable and Privacy-preserving Population Genomics, Nature Computational Science, 2021, Mar;1(3):192-8
Scheibner, M. Ienca, and E. Vayena, Whose health record?: A Comparison of Patient Rights under National Electronic Health Record (NEHR) Regulations in Europe and Asia-pacific Jurisdictions, Singapore Journal of Legal Studies, Apr 2021:56-75
Kim, A. Harmanci, J-P. Bossuat, S. Carpov, J. H. Cheon, I. Chillotti, W. Cho, D. Froelicher, N. Gama, M. Georgieva, S. Hong, J-P. Hubaux, D. Kim, K. Lauter, Y. Ma, L. Ohno-Machado, H. Sofia, Y. Son, Y. Song, JR. Troncoso-Pastoriza, and X. Jiang, Ultra-Fast Homomorphic Encryption Models enable Secure Outsourcing of Genotype Imputation, Cell Systems, 2021
Mouchet, JR. Troncoso-Pastoriza, J-P. Bossuat, and J-P. Hubaux, Multiparty Homomorphic Encryption from Ring-Learning-With-Errors, in Proceedings on Privacy Enhancing Technologies (PETS), 2021
Froelicher, JR. Troncoso-Pastoriza, A. Pyrgelis, S. Sav, J. Sa Sousa, J-P. Bossuat, and J-P. Hubaux, Scalable Privacy-Preserving Distributed Learning, in Proceedings on Privacy Enhancing Technologies (PETS), 2021
Scheibner, J. Sleigh, M. Ienca, and E. Vayena, Benefits, Challenges, and Contributors to Success for National eHealth Systems Implementation: A Scoping Review, Journal of the American Medical Informatics Association, Volume 28, Issue 9, September 2021, Pages 2039–2049
Senf, R. Davies, F. Haziza, J. Marshall, JR. Troncoso-Pastoriza, O. Hofmann, T. M. Keane, Crypt4GH: a File Format Standard Enabling Native Access to Encrypted Data, Bioinformatics, Volume 37, Issue 17, 1 September 2021, Pages 2753–2754
Froelicher, JR. Troncoso-Pastoriza, JL. Raisaro, M. Cuendet, J. Sa Sousa, H. Cho, B. Berger, J. Fellay, and J-P. Hubaux, Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption, Nature Communications 12, 5910, 2021
Froelicher, M. Misbach, JR. Troncoso-Pastoriza, JL. Raisaro, and J-P. Hubaux, MedCo2: Privacy-Preserving Cohort Exploration and Analysis, Studies in Health Technology and Informatics, 2020 Jun 1;270:317-21
Raisaro, JR. Troncoso-Pastoriza, Y. El-Zein, M. Humbert, C. Troncoso, J. Fellay, and J-P. Hubaux, GenoShare: Supporting Privacy-Informed Decisions for Sharing Individual-Level Genetic Data, Studies in Health Technology and Informatics, 2020, Jun 1;270:238-41
Scheibner, M. Ienca, S. Kechagia, JR. Troncoso-Pastoriza, JL. Raisaro, J-P. Hubaux, and E. Vayena, Data Protection and Ethics Requirements for Multisite Research with Health Data: A Comparative Examination of Legislative Governance Frameworks and the Role of Data Protection Technologies, Journal of Law and the Biosciences, 2020
Raisaro, JR. Troncoso-Pastoriza, S. Pradervand, M. Cuendet, M. Misbach, J. Sa, F. Marino, N. Freundler, N. Rosat, D. Cavin, A. Leichtle, J. Fellay, O. Michielin, and J-P. Hubaux, SPHN/PHRT-MedCo in Action: Empowering the Swiss Molecular Tumor Board with Privacy-Preserving and Real-Time Patient Discovery, Studies in Health Technology and Informatics, 2020 Jun 1;270:1161-2
Scheibner, JL. Raisaro, JR. Troncoso-Pastoriza, M. Ienca, J. Fellay, E. Vayena, and J-P. Hubaux, Revolutionizing Medical Data Sharing Using Advanced Privacy Enhancing Technologies: Technical, Legal and Ethical Synthesis, Journal of Medical Internet Research, 2021
Froelicher, JR. Troncoso-Pastoriza, JL. Raisaro, M. Cuendet, J. Sa Sousa, H. Cho, B. Berger, J. Fellay, and J-P. Hubaux, Truly Privacy-Preserving Federated Analytics for Precision Medicine with Multiparty Homomorphic Encryption, Nature Communications 12, 5910, 2021
Boura, N. Gama, M. Georgieva, and D. Jetchev, Chimera: Combining Ring-lwe-based Fully Homomorphic Encryption Schemes, Journal of Mathematical Cryptology, Jan 1;14(1):316-38, 2020
Chillotti, N. Gama, M. Georgieva, and M. Izabachène, TFHE: Fast Fully Homomorphic Encryption Over the Torus, Journal of Cryptology, Jan;33(1):34-91, 2020
Ienca and E. Vayena, On the responsible use of digital data to tackle the COVID-19 pandemic. Nature Medicine, 26(4), 463-464, 2020
Gasser, M. Ienca, J. Scheibner, J. Sleigh, and E. Vayena, Digital tools against COVID-19: Taxonomy, Ethical Challenges, and Navigation Aid, The Lancet Digital Health, 2020
Ienca and J. Scheibner, What is Neurohacking? Defining the Conceptual, Ethical and Legal Boundaries, Ethical Dimensions of Commercial and DIY Neurotechnologies, 3, 203, 2020
Scheibner, A. Jobin, and E. Vayena, Ethical Issues with Using Internet of Things Devices in Citizen Science Research: A Scoping Review, Cambridge Handbook of Life Science, Information Technology and Human Rights, 2020
Gosselin, C. Redin, É. Ristorcelli, JL. Raisaro, and J. Fellay, Precision Medicine: at the Crossroad of Molecular Biology, Information Sciences and Humanities, Revue Medicale Suisse, 2020 Sep ;16(704):1574-8
Argaw, JR. Troncoso-Pastoriza, D. Lacey, MV. Florin, F. Calcavecchia, D. Anderson, W. Burleson, JM. Vogel, C. O’Leary, B. Eshaya-Chauvin, and A. Flahault, Cybersecurity of Hospitals: Discussing the Challenges and Working Towards Mitigating the Risks, BMC Medical Informatics and Decision Making, 2020 Dec;20(1):1-0
Froelicher, JR. Troncoso-Pastoriza, JS Sousa, and J-P. Hubaux, Drynx: Decentralized, Secure, Verifiable System for Statistical Queries and Machine Learning on Distributed Datasets, in IEEE Transactions on Information Forensics and Security, 2020 Mar 2;15:3035-50
JL Raisaro, F. Marino, JR. Troncoso-Pastoriza, et al. SCOR: A Secure International Informatics Infrastructure to Investigate COVID-19, in Journal of the American Medical Informatics Association, Volume 27, Issue 11, November 2020, Pages 1721–1726
Baum, D. Escudero, A. Pedrouzo-Ulloa, P. Scholl P, and JR. Troncoso-Pastoriza, Efficient Protocols for Oblivious Linear Function Evaluation from Ring-LWE, Journal of Computer Security, IOS Press, January, 2021
Pedrouzo-Ulloa, JR. Troncoso-Pastoriza, N. Gama, M. Georgieva, and F. Pérez-González, Revisiting Multivariate Ring Learning with Errors and Its Applications on Lattice-Based Cryptography, Mathematics, 2021; 9(8):858
Chatel, A. Pyrgelis, JR. Troncoso-Pastoriza, and J-P. Hubaux, SoK: Privacy-Preserving Collaborative Tree-based Model Learning, in Proceedings on Privacy Enhancing Technologies (PETS), 2021

Patents / Startups

Tune Insight

Encrypted computing – collective analytics, machine learning & AI

Despite the ever increasing data-dependance for all critical business decisions and the never ending need of data to feed artificial intelligence, companies are prevented from collaborating on and valorizing sensitive data because of cyber risks, fear of losing competitive edge and regulatory constraints. Tune Insight helps organizations to overcome this hurdle, providing an encrypted computing platform for them to automate collective intelligence extraction, to reduce data liability, and to streamline compliance, while re-enforcing data security and privacy.