关键词: 隐私保护, 混淆模分量, 同态加密, 云计算, 批处理机制

Abstract: With the rapid proliferation of cloud computing, big data, and InternetofThings technologies, data privacy and security concerns have become increasingly prominent, while traditional dataprocessing methods exhibit inherent limitations in safeguarding sensitive information. Homomorphic encryption (HE) offers a promising privacypreserving approach by enabling computations to be performed directly on encrypted data. However, existing schemes typically suffer from high computational complexity, significant ciphertext expansion, and substantial resource consumption, which impede their practical deployment. To address these challenges, this paper proposes an efficient confused modulo projectionbased fully homomorphic encryption (EffiCMPFHE) algorithm. Leveraging the Chinese Remainder Theorem for multimodular redundant encoding of plaintexts, the scheme introduces streamlined encryption, blindcomputation, and decryption procedures to reduce processing overhead. Moreover, to accommodate largescale data workloads, this paper develops a batching mechanism that aggregates multiple messages into a single large integer for parallel evaluation, thereby significantly reducing overall computation time. To facilitate adoption, this paper also designs and implements a generalpurpose homomorphic encryption library based on EffiCMPFHE and benchmarks it against mainstream FHE frameworks. Experimental results demonstrate that the library achieves a marked improvement in operational speed. This work provides an efficient and practical pathway for applying homomorphic encryption in dataprivacy protection, cloud computing, and secure multiparty computation environments.

Key words: privacy protection, confused modulo projection, fully homomorphic encryption, cloud computing, batch processing mechanism