Project

Data Protection in Personalized Health (DPPH)

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

Patents / Startups

Publications