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

摘要： 随着云计算、大数据和物联网技术的迅速普及，数据隐私与安全问题日益突出，而传统的数据处理方法在保障数据安全方面存在局限.同态加密(homomorphic encryption, HE)是一种隐私保护方法，允许数据在处于加密状态下直接进行运算.尽管理论上同态加密具有巨大潜力，但现有方案普遍存在计算复杂度高、密文膨胀严重及资源消耗大的问题，从而制约了其实际应用.为此，提出了一种高效的基于混淆模分量的全同态加密(efficient confused modulo projectionbased fully homomorphic encryption, EffiCMPFHE)算法.该算法利用中国剩余定理对原始消息进行多模冗余编码，并设计了高效的加密、同态计算及解密流程降低运算开销.同时，针对大规模数据处理需求，构建了批处理机制，将多个消息合并为单一大整数进行并行运算，有效缩短了处理时间.为了使该算法易于使用，还设计并实现了一个基于EffiCMPFHE的通用同态加密库，并与主流全同态加密(fully homomorphic encryption, FHE)库进行了比较.实验结果表明，该库在运算速度上具有显著优势.该研究为同态加密技术在数据隐私保护、云计算和安全多方计算等领域的应用提供了一条高效且实用的技术路径.

关键词: 隐私保护, 混淆模分量, 同态加密, 云计算, 批处理机制