FHE Computation Vulnerable to Silent Data Corruption on Real Hardware

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As Fully Homomorphic Encryption moves toward production deployment in secure finance, biomedical analytics, and privacy-preserving AI, this paper reveals that FHE computation is significantly more susceptible to silent data corruption (SDC) on real hardware than plaintext computation. FHE's noise-accumulating ciphertext arithmetic amplifies the impact of bit flips and memory errors that would be benign in normal computation. This is critical for anyone deploying privacy-preserving ML inference in cloud environments where hardware errors are statistically inevitable at scale.