Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models
Published in IEEE Access, 2025
This work introduces a secure framework for running clinically actionable ML models under homomorphic encryption. The system achieves high accuracy while ensuring full data privacy, demonstrated with healthcare datasets.
Recommended citation: Balaban, B., Magara, S. S., Yilgor, C., Yucekul, A., Obeid, I., Pizones, J., Kleinstueck, F., Perez-Grueso, F. J. S., Pellisé, F., Alanay, A., Savas, E., Bağcı, Ç., Sezerman, O. U., & European Spine Study Group. (2025). Privacy-Preserving Machine Learning (PPML) Inference for Clinically Actionable Models. IEEE Access, 13, 37431-37456.
Download Paper