Feature-Aware Anisotropic Local Differential Privacy for Utility-Preserving Graph Representation Learning in Metal Additive Manufacturing

1. 1Apply feature-aware anisotropic local differential privacy to graph representation learning
2. 2Utilize layer-wise physical couplings to improve defect-detection models
3. 3Implement utility-preserving techniques to protect proprietary process information
4. 4Evaluate the approach in metal additive manufacturing applications

Data scientists and machine learning engineers on a team can benefit from this approach to improve the privacy and utility of graph representation learning in metal additive manufacturing, while collaborating with domain experts in the field

💡 The proposed approach can balance privacy and utility in graph representation learning for metal additive manufacturing

🔒💡 Feature-aware anisotropic local differential privacy for utility-preserving graph representation learning in metal additive manufacturing