Presentation
Runtime-ADAR: Runtime Anomaly Detection and Attack Recovery in Encrypted DNNs
DescriptionFault injection attack (FIA) poses a severe threat to accelerators executing privacy-preserving homomorphic encryption (HE) neural networks. Defending against FIAs in the ciphertext domain is challenging due to opaque internal states and the difficulty of distinguishing inherent encryption noise from malicious perturbations. We propose Runtime-ADAR, a lightweight runtime framework enabling closed-loop detection and recovery fully within the ciphertext domain. By repurposing intrinsic HE noise as a high-fidelity diagnostic signal, Runtime-ADAR integrates a sparse-projection noise detector for statistical anomaly sensing and a gradient-field repair engine for in-situ weight compensation. This synergy transforms encryption noise from a vulnerability into a defensive asset. Evaluations on multiple encrypted models show that Runtime-ADAR mitigates over 90% of attack successes and restores up to 97% baseline accuracy with modest performance overhead on CNNs.
Event Type
Research Manuscript
TimeMonday, July 2711:10am - 11:23am PDT
LocationMtg Room 203C