A Hierarchical Benchmarking Framework for Homomorphic Encryption-Based Aggregation and Validation Using Real Smart Meter Data

Abstract

Homomorphic encryption (HE) enables computation on encrypted data and has emerged as a promising technology for privacy-preserving distributed analytics. However, the practical deployment of HE in large-scale hierarchical systems requires a thorough understanding of its computational overhead, scalability, and accuracy. This paper presents a generic hierarchical benchmarking framework for systematically evaluating homomorphic encryption schemes in multi-level aggregation environments. The framework supports configurable aggregation topologies, detailed operation-level profiling, and multiple encryption backends, enabling consistent and reproducible performance analysis across node-, cluster-, and global-level aggregation stages. Using the proposed framework, we conduct a comparative evaluation of the Brakerski/Fan–Vercauteren (BFV) and Cheon–Kim–Kim–Song (CKKS) schemes under identical workloads. Experimental results show that CKKS consistently outperforms BFV, achieving a 44.3% reduction in aggregation latency and a 24.6% reduction in decryption latency. For the tested encoding and parameter settings, CKKS delivers significantly lower numerical error, reducing the mean absolute error from 3.21×10−3 to 5.91×10−10. The proposed framework offers a reusable and extensible platform for evaluating emerging HE schemes and privacy-preserving analytics applications, thereby supporting future research and deployment of secure distributed data processing systems.