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.

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

7. Data curation
Quality
Subject attire
Accuracy
Resolution
Acquisition time

8. Interoperability problems. Resolution, formats and
metadata

9. Interoperability problems. 3D is not a table
1
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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

14. The anonymization
problem

15. Synthetic avatars
RAW DEFACED SYNTHETIC FACE

16. The BODYPASS
ecosystem

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

20. BODYPASS ecosystem

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