My Health – My Data
MyHealthMyData (MHMD) was a Horizon 2020 Research and Innovation Action which aimed at fundamentally changing the way sensitive data are shared.
MHMD developed an open biomedical information network centered on the connection between organizations and individuals, encouraging hospitals to start making anonymized data available for open research, while prompting citizens to become the ultimate owners and controllers of their health data. MHMD is intended to become a true information marketplace, based on new mechanisms of trust and direct, value-based relationships between EU citizens, hospitals, research centres and businesses.
Key elements of this innovation, implemented through this new model, include:
- Privacy-preserving data publishing via synthetic data
- Dynamic consent
- Personal data accounts
- Smart contracts
- Multilevel de-identification and encryption technologies
- Secure multi-party computation (SMPC)
- Homomorphic encryption
- Federated learning
SBA Research focused on anonymization techniques, a secure exchange system, and methods for watermarking and fingerprinting relational data.
- Privacy-preserving anomaly detection using synthetic data (2020). Rudolf Mayer, Markus Hittmeir and Andreas Ekelhart. Conference on Data and Applications Security and Privacy (DBSec). PDF
- A Baseline for Attribute Disclosure Risk in Synthetic Data (2020). Markus Hittmeir and Andreas Ekelhart and Rudolf Mayer. 10th ACM Conference on Data and Application Security and Privacy (CODASPY). PDF
- Utility and privacy assessments of synthetic data for regression tasks (2019). Markus Hittmeir and Andreas Ekelhart and Rudolf Mayer. IEEE International Conference on Big Data (IEEE BigData 2019). PDF
- On the utility of synthetic data: An empirical evaluation on machine learning tasks (2019). Markus Hittmeir and Andreas Ekelhart and Rudolf Mayer. 14th Conference on Availability, Reliability and Security (ARES 2019). PDF
- An Evaluation on Robustness and Utility of Fingerprinting Schemes (2019). Tanja Šarčević and Rudolf Mayer. International Cross-Domain Conference for Machine Learning and Knowledge Extraction. PDF
- The project was led by Lynkeus.
- Public Deliverables
- Website, Twitter, Facebook, Research Gate, Zenodo
Related News & Events
This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 732907.