Dataflow-Oriented Classification and Performance Analysis of GPU-Accelerated Homomorphic Encryption

2026-03-17

AI Summary

This work addresses a critical gap in existing GPU-accelerated implementations of the CKKS homomorphic encryption scheme: the neglect of how parameter configuration impacts memory usage and computational efficiency, often leading to suboptimal performance. For the first time, this study reveals a strong interdependence between CKKS parameters and GPU optimization strategies. It proposes a classification framework grounded in dataflow characteristics and establishes parameter-aware strategy selection guidelines through quantitative performance analysis across diverse GPU architectures. Experimental results demonstrate that, on identical hardware, performance can vary by up to 1.98× depending on the chosen strategy, thereby validating the necessity and effectiveness of the proposed approach for guiding high-performance CKKS implementations.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Algorithm Configuration

Application Category

• Security and Privacy: Applications of cryptography
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Fully Homomorphic Encryption (FHE) enables secure computation over encrypted data, but its computational cost remains a major obstacle to practical deployment. To mitigate this overhead, many studies have explored GPU acceleration for the CKKS scheme, which is widely used for approximate arithmetic. In CKKS, CKKS parameters are configured for each workload by balancing multiplicative depth, security requirements, and performance. These parameters significantly affect ciphertext size, thereby determining how the memory footprint fits within the GPU memory hierarchy. Nevertheless, prior studies typically apply their proposed optimization methods uniformly, without considering differences in CKKS parameter configurations. In this work, we demonstrate that the optimal GPU optimization strategy for CKKS depends on the CKKS parameter configuration. We first classify prior optimizations by two aspects of dataflows which affect memory footprint and then conduct both qualitative and quantitative performance analyses. Our analysis shows that even on the same GPU architecture, the optimal strategy varies with CKKS parameters with performance differences of up to 1.98 $\times$ between strategies, and that the criteria for selecting an appropriate strategy differ across GPU architectures.

Problem

• Homomorphic Encryption
• CKKS
• GPU acceleration
• dataflow
• parameter configuration

Innovation

• dataflow-oriented classification
• GPU-accelerated homomorphic encryption
• CKKS parameter configuration
• memory hierarchy optimization
• performance analysis