Paper 2026/362

Janus-FHE: Reducing Microarchitectural Leakage in GPU-Based Homomorphic Encryption

Abstract

Homomorphic Encryption (HE) enables secure cloud computing through computations on encrypted data, but the physical execution of HE workloads on shared GPUs can still expose relevant metadata through microarchitectural behavior. Implementation-level irregularities in key switching, rounding, and modular correction may create observable hardware footprints even when cryptographic confidentiality remains intact. We present a case study of BFV relinearization in a state-of-the-art GPU HE library HEonGPU showing that certain execution patterns exhibit structured, reproducible variation in shared-cache footprint tied to a specific implementation mechanism, creating a setup-specific fingerprinting surface for a co-resident observer. We measure this signal using a controlled diagnostic profiling instrument. Motivated by this finding, we present JANUS-FHE, a GPU framework for BFV multiplication and relinearization designed around execution regularity as a first-class objective. JANUS reformulates ciphertext multiplication through Kronecker substitution and structured large-integer arithmetic, executed using a Schonhage-Strassen-style pipeline based on the Discrete Galois Transform (DGT); a Stockham formulation regularizes memory access across transform stages, and relinearization is expressed through fixed execution geometry and branchless masked arithmetic, eliminating the class of value-dependent control flow identified in the case study. We evaluate JANUS under the identical shared-cache methodology used to characterize the HEonGPU leak: key-conditioned differences that reproduce consistently across independent measurements for HEonGPU do not reproduce for JANUS, and JANUS' masked arithmetic is confirmed branchless at the compiled-instruction level. JANUS is a mitigation-oriented BFV multiplication-relinearization framework and a first step toward secure GPU-based FHE design, reducing leakage-relevant execution variability in shared-GPU environments while remaining practical across large parameter sizes.