The challenges of 3D Personal Data
The challenges of 3D Personal Data
How to exchange and grant access to health data in a secure way approaches and recommendations The health crisis due to COVID-19 is shaping a new reality in which the exchange and access to health data in a secure way will be more and more necessary. In this complex challenge converge both the respect for the individual rights as well as the interests of the patients and the need to promote the research in pursuit of the public interest. To face this challenge, we can find different approaches across Europe. In this webinar, we will present the experiences of three EU-funded projects (BigMedilytics, BodyPass, and DeepHealth), besides an overview of the legal framework and recommendations to enforce both national regulations and GDPR by an expert in data privacy and security.
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The challenges of 3D Personal Data
- 1. “This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779780”. The challenges of 3D Personal Data Juan V. Durá juan.dura@ibv.org 14th May 2020
- 2. What is 3D personal data?
- 3. What is 3D personal data?
- 4. Two silos Health sector and Consumer goods. Why to share data? • The 3D data of the health sector contains the body shape information, not only internal body information. These data could be used by designers and manufacturers of the consumer goods sector. • The 3D scanners’ in the consumer goods sector are low cost, non-invasive, and ease of use. It makes them appealing for widespread clinical applications and large-scale epidemiological surveys.
- 5. Why is big data? • Hospitals: About 2.7 petabytes a year stored in the EU26 • Consumer goods: it is estimated that currently one person is scanned every 15 minutes in the US and Europe.
- 6. BodyPass objectives • Generate tools for extraction of 3D model data from raw 3D scans, and medical imaging data. • Generate protocols and data models for privacy preserving and secure exchange of extracted and derived data between different parties enabling data exchange and big data analytics across different silos
- 8. Interoperability problems. Resolution, formats and metadata
- 9. Interoperability problems. 3D is not a table 1 𝑛 𝑖=1 𝑛 𝑋𝑖
- 10. Solution: The Standard Template Point-to-point correspondence Body pats (ISO 8559-1)
- 11. Same pose Goal: Adapt the pose for aggregated data
- 12. Goal: obtain reliable/usable metrics for end-users Digital Measuring Step 1: landmarks location
- 13. Goal: obtain reliable/usable metrics Digital Measuring Step 2: measuring
- 15. Synthetic avatars RAW DEFACED SYNTHETIC FACE
- 17. Transactions
- 18. Query Types • Type 0: Post one individual 3D data (DATA CURATION) • Type A: Get one individual 3D data & metrics • Type B: Get many anonymous individual 3D data & metrics • Type C: Get aggregated 3D data & metrics Each data provider chooses which queries it responds to
- 19. Query Types • Type 0: Post one individual 3D data (DATA CURATION) • Type A: Get one individual 3D data & metrics • Type B: Get many anonymous individual 3D data & metrics • Type C: Get aggregated 3D data & metrics Hospitals aggregated data only
- 21. Off-chain components: Data Management Plane
- 22. Consortium
- 23. http://bodypass.eu/ This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 779780 Part of the Big Data Value Public-Private Partnership juan.dura@ibv.org