Paper 2026/362

Janus-FHE: A Side Channel Resilient Framework for High-Degree Homomorphic Encryption on GPUs

Nektarios Georgios Tsoutsos, University of Delaware

Abstract

Homomorphic Encryption (HE) enables secure cloud computing through computations on encrypted data, yet the physical security of these implementations on shared hardware accelerators remains a critical challenge. While Graphics Processing Units (GPUs) offer the massive parallelism required for HE workloads, their Single Instruction Multiple Thread (SIMT) architecture amplifies side-channel vulnerabilities. Standard implementations of polynomial multiplication and relinearization often exhibit data-dependent control flows and irregular memory access patterns that leak sensitive information through variable timing behavior. In this paper, we present Janus-FHE, a GPU-based framework for BFV ciphertext multiplication and relinearization designed with intrinsic side-channel resistance. We reformulate polynomial multiplication as large-integer arithmetic via Kronecker substitution, executing it using a Schonhage-Strassen algorithm based on the Discrete Galois Transform (DGT). Critically, we compute these transforms using the Stockham algorithm, which enforces strictly deterministic, input-independent memory access patterns, effectively mitigating cache-timing vulnerabilities. Furthermore, we implement a constant-time relinearization strategy that replaces conditional branching with masked arithmetic to prevent warp divergence. Our experimental evaluation confirms that Janus-FHE eliminates the control-flow leakage observed in state-of-the-art libraries like HEonGPU, extending the computational reach of GPU-based FHE by successfully computing multiplications for polynomial degrees up to $2^{18}$.