Secure Multi-Party Computation for Magnetic Resonance Imaging Classification

Accessing private data always requires a complex negotiation process. This process becomes even more challenging under privacy regulations such as the General Data Protection Regulation and California Consumer Privacy Act. However, in most cases, availability of greater amounts leads to significant technological breakthroughs. The perfect example is ImageNet classification challenge, which improvements image recognition area. combination these concerns raises question performing computations on that cannot be seen, whole new research field consolidates privacy-preserving concepts modern mining techniques comes into place. also purpose work presented this paper, discovery Secure Multi-Party Computation (SMPC) capabilities protecting during machine learning main attention paid towards SMPC approach based convolutional neural network, especially secure inference encrypted magnetic resonance images.