Fully Homomorphic Encryption (FHE) enables computation on encrypted data with end-to-end confidentiality; however, its practical adoption remains limited by substantial computational costs, including long encryption and decryption times, high memory consumption, and operational latency. Zero-Knowledge Proofs (ZKPs) complement FHE by enabling correctness verification without revealing sensitive information. This study integrates both techniques to enable encrypted computation with verifiably consistent results. A prototype system is implemented in Python using Microsoft SEAL and PySNARK. Experimental results show that integrating ZKPs introduces a stable runtime overhead of approximately 15–20% while enabling verification without plaintext disclosure. The system demonstrates feasible scalability for datasets up to 1 GB on mid-level hardware.

Keywords: Cryptography; Fully Homomorphic Encryption; Microsoft SEAL; Privacy-Preserving Systems; Python; Secure Computation; Zero-Knowledge Proofs; zk-SNARK.