Guaranteeing Privacy in Hybrid Quantum Learning through Theoretical Mechanisms

Documenti analoghi

• Analysis of Privacy Leakage in Federated Large Language Models
  di: Vu, Minh N., et al.
  Pubblicazione: (2024)
• OASIS: Offsetting Active Reconstruction Attacks in Federated Learning
  di: Jeter, Tre' R., et al.
  Pubblicazione: (2023)
• Q-ShiftDP: A Differentially Private Parameter-Shift Rule for Quantum Machine Learning
  di: Ngo, Hoang M., et al.
  Pubblicazione: (2026)
• Towards A Hybrid Quantum Differential Privacy
  di: Song, Baobao, et al.
  Pubblicazione: (2025)
• Preserving Privacy and Security in Federated Learning
  di: Nguyen, Truc, et al.
  Pubblicazione: (2022)