Offloading Key Switching on GPUs: A Path towards Seamless Acceleration of FHE
Year: 2025
Type: conference-paper
Abstract: Fully Homomorphic Encryption (FHE) enables secure computations on encrypted data, offering strong privacy guarantees for cloud computing, privacy-preserving machine learning, and confidential data processing. However, the computational overhead associated with FHE operations, due to the large size of ciphertext and the high arithmetic complexity, limits its practical applicability.In this work, we address this challenge by presenting an approach that is implemented within the OpenFHE library in order to offload the most dominant components of key switching for the BGV scheme on GPU hardware. In particular, the scope of this work is the performance improvement of the Approximate Modulus Downscaling (ApproxModDown) function. Our experimental evaluation shows that the proposed system can yield up to a 4.58× performance speedup against the vanilla OpenFHE ApproxModDown implementation, while also resulting in 1.16× performance mprovement per homomorphic multiplication and 1.08× improvement for end-to-end execution time.
Authors: Papadakis, Orion, Papadimitriou, Michail, Stratikopoulos, Athanasios, Xekalaki, Maria Nektaria, Fumero, Juan, Kotselis, Christos
Institution: University of Manchester
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W6968419757
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