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. Predicting heart failures using Multi-Party Computation
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H2020 BigMedilytics Project with Erasmus MC, Achmea and TNO
Pilot Topic Heart Failure
Alex Sangers (alex.sangers@tno.nl)

2. BIGMEDILYTICS HEART FAILURE PILOT:
DOES MY HEART FAILURE PATIENT REQUIRE ADDITIONAL CARE?
1. Heart failure patient arrives at the hospital
2. Doctor measures and requests data about
this patient
3. Doctor uses the patients data and a prediction model to determine whether
this patient requires additional health interventions
However:
• For accurate predictions, the prediction model is trained based on many patient data of
multiple organizations
• And getting consent from the large group of patients is time consuming.

3. COMBINING PATIENT INFORMATION OF MULTIPLE
ORGANIZATIONS RESULTS IN BETTER PREDICTIONS
But there are reasons not to share data
• Privacy concerns / bad publicity
• Laws and regulations (e.g. GDPR)
• Business sensitive information
• Revealing suboptimal business processes
• …
Smoking behaviour Claims data w.r.t.
heart failure
Alcohol behaviour Claim data w.r.t.
other diseases
Exercising …
… …
Table: available data at different organizations for
(partially) the same persons

4. Goal: Identify heart failure patients with a high risk of early hospitalization
or mortality, so they can receive additional health care interventions
How: Develop accurate hospitalization prediction model based on
combination of insurance data and hospital data
Problem: Sensitive data that cannot be shared!
DATA SHARING CHALLENGE: HOW TO DEVELOP AN ACCURATE
PREDICTION MODEL?
?Hospital data Insurance data

5. BREAKING THE PARADOX: WE WANT A MODEL, NOT DATA
Privacy
&
Confidentiality
Information sharing
&
Cooperation
Exercise (hours/week)
[Erasmus MC]
= input data on heart
failure patients (sensitive!)
= resulting prediction
model (not sensitive)

6. MULTI-PARTY COMPUTATION (MPC): COOPERATIVELY
ANALYZING DATA, WITHOUT SHARING DATA
MPC uses cryptographic protocols to:
• Cooperatively perform a
computation
• Without sharing sensitive data
• And receive only the outcome of
the computation
𝑥4
𝑥2𝑥1
𝑥3
𝑓(𝑥1, 𝑥2, 𝑥3, 𝑥4)

7. Based on advanced cryptography
No one-size-fits-all solution:
• Toolbox with different cryptografic techniques
Trade-off between:
• Functionality
• Computation time
• Communication / bandwidth
• Security
Last years: improved practical applicability, including
software libraries
MULTI-PARTY COMPUTATION IS CHALLENGING

8. A PREDICTION MODEL CAN BE TRAINED BY APPLYING
DATABASES OF MULTIPLE ORGANIZATIONS THAT ARE
COUPLED VIA MULTI-PARTY COMPUTATION
Laptop/
server/VM
Encrypted
data
Encrypted
data
Laptop/
server/VM
MPC
MPC
MPC
Laptop/
server/VM
Developed by:

9. Big Impact all together with