This paper addresses vulnerabilities in standard federated learning aggregation against gradient inversion attacks by evaluating three layered privacy-enhancing mechanisms: classical differential privacy (DP) noise injection, homomorphic encryption, and a novel quantum-inspired random unitary rotation of embedding vect...
This paper addresses vulnerabilities in standard federated learning aggregation against gradient inversion attacks by evaluating three layered privacy-enhancing mechanisms: classical differential privacy (DP) noise injection, homomorphic encryption, and a novel quantum-inspired random unitary rotation of embedding vect...
This paper introduces a unified, three-layer converged security architecture designed to shield critical digital infrastructure from both classical cyber threats and emerging quantum decryption risks. The architecture integrates a DNA-steganography post-quantum key exchange scheme to secure inter-layer communications,...
This paper introduces a unified, three-layer converged security architecture designed to shield critical digital infrastructure from both classical cyber threats and emerging quantum decryption risks. The architecture integrates a DNA-steganography post-quantum key exchange scheme to secure inter-layer communications,...