On the Sensitivity to Errors in Homomorphic Computing: Single Transient Bit-flip Client-side Error Characterization
2026-08-11 • Hardware Architecture
Hardware ArchitectureCryptography and Security
AI summaryⓘ
The authors studied how Homomorphic Encryption (HE), which lets people work on encrypted data without unlocking it, reacts to tiny mistakes called bit-level faults. They focused on the CKKS scheme used in AI calculations and found that the multiplication step is especially sensitive to errors. These faults can spread and grow, making the system less reliable. Their work shows that HE systems need better protection to safely handle such errors.
Homomorphic EncryptionCKKS schemebit-level faultsfault tolerancehomomorphic multiplicationapproximate arithmeticencrypted computationerror sensitivity
Authors
Matías Mazzanti, Vattana Chan, Karthik Swaminathan, Augusto Vega, Esteban Mocskos, Radha Venkatagiri
Abstract
Homomorphic Encryption (HE) enables computation on encrypted data without decryption and is a key primitive for privacy-preserving computation in sensitive domains such as healthcare, finance, and government. Its security relies on noise injection, which introduces intrinsic error sensitivity and raises concerns about the fault tolerance of HE systems, as hardware- and software-induced faults can evade traditional detection mechanisms and lead to silent data corruption. In this work, we analyze the sensitivity of HE to bit-level faults, focusing on the CKKS (Cheon--Kim--Kim--Song) scheme widely used for approximate arithmetic in AI and machine learning workloads. We identify homomorphic multiplication as the most error-sensitive operation in practical HE pipelines and characterize how faults propagate and amplify through it, exposing a critical robustness vulnerability and motivating the need for more resilient HE deployments.