A Performance Driven Decision Framework for Hybrid Homomorphic Encryption

Abstract

: To support privacy-preserving computation in resource-constrained environments, this paper presents a systematic performance comparison between homomorphic encryption (HE) and hybrid homomorphic encryption (HHE) on embedded edge hardware. While HE enables computations on encrypted data without decryption, its practical implementation remains challenging due to extensive computational and memory requirements. HHE alleviates this burden by having the client encrypt data with the lightweight symmetric cipher instead, while the data processor (server) handles the intensive transciphering into homomorphic format. We investigate the performance trade-offs between direct encryption with the homomorphic encryption scheme TFHE and a hybrid approach combining the Kreyvium stream cipher with TFHE. Testing on Raspberry Pi hardware shows HHE’s client-side advantages: Kreyvium’s pure encryption time is two times faster than TFHE, and it requires only 0.02% of its RAM. This efficiency comes at the cost of increased server-side processing during transciphering, shifting the computational burden from encryptors (clients) to data processors (servers). Based on measurements across different hardware configurations, we develop a decision framework for selecting HHE or HE based on data characteristics and operational requirements.