Privacy-Preserving and Secure Manufacturing
Federated learning, secure computation, ethical fabrication, and manufacturing cybersecurity testbeds.
Research Focus
The lab designs privacy-preserving methods that allow manufacturers to learn from distributed data without exposing sensitive information. Research spans federated learning, homomorphic encryption, secure multi-party computation, ethical fabrication, and cyber-physical manufacturing security.
Applications
- Privacy-preserving illegal-product detection in digital fabrication.
- Secure data sharing across distributed manufacturing partners.
- Hybrid cyber-physical testbeds for manufacturing-security research and training.
Impact
Secure and privacy-preserving AI helps manufacturers collaborate, detect risks, and protect intellectual property while still benefiting from data-driven models.