Privacy-Preserving Graph-Based Machine Learning with Fully Homomorphic Encryption for Collaborative Anti-Money Laundering

Ähnliche Einträge

• Comparison of Fully Homomorphic Encryption and Garbled Circuit Techniques in Privacy-Preserving Machine Learning Inference
  von: Cheerla, Kalyan, et al.
  Veröffentlicht: (2025)
• FedGraph-VASP: Privacy-Preserving Federated Graph Learning with Post-Quantum Security for Cross-Institutional Anti-Money Laundering
  von: Commey, Daniel, et al.
  Veröffentlicht: (2026)
• GuardML: Efficient Privacy-Preserving Machine Learning Services Through Hybrid Homomorphic Encryption
  von: Frimpong, Eugene, et al.
  Veröffentlicht: (2024)
• Efficient Privacy-Preserving Recommendation on Sparse Data using Fully Homomorphic Encryption
  von: Chowdhury, Moontaha Nishat, et al.
  Veröffentlicht: (2025)
• Efficient Privacy-Preserving KAN Inference Using Homomorphic Encryption
  von: Lai, Zhizheng, et al.
  Veröffentlicht: (2024)