The demand for processing vast volumes of data has surged dramatically due to the advancement of machine learning technology. Large-scale data processing necessitates substantial computational resources, prompting individuals and enterprises to turn to cloud services. Accompanying this trend is a growing concern regarding data leakage and misuse. Homomorphic encryption (HE) is one solution for safeguarding data privacy, enabling encrypted data to be processed securely in the cloud. However, we observe that encryption and decryption routines of some HE schemes require considerable computational resources, presenting non-trivial work for clients. In this paper, we propose an outsourced decryption protocol for RLWE-based HE schemes, which splits the original decryption into two routines, with the computationally intensive part executed remotely by the cloud. Its security relies on an invariant of the NTRU-search problem with a newly designed secret distribution. Cryptographic analyses are conducted to configure protocol parameters across varying security levels. Our experiments demonstrate that the proposed protocol achieves up to a $67\%$ acceleration in the client's local decryption, accompanied by a $50\%$ reduction in space usage.

翻译：随着机器学习技术的发展，对海量数据处理的需求急剧增长。大规模数据处理需要大量计算资源，促使个人和企业转向云服务。与此同时，数据泄露和滥用的担忧也日益加剧。同态加密是保护数据隐私的一种解决方案，它使得加密数据能够在云端安全地进行处理。然而，我们观察到某些同态加密方案的加密与解密例程需要大量计算资源，给客户端带来了不小的负担。本文针对基于RLWE的同态加密方案提出了一种外包解密协议，该协议将原始解密过程拆分为两个例程，其中计算密集型部分由云端远程执行。其安全性依赖于采用新设计秘密分布的NTRU搜索问题的不变性。我们通过密码学分析来配置不同安全级别下的协议参数。实验结果表明，所提出的协议在客户端本地解密方面实现了高达$67\%$的加速，同时空间使用量减少了$50\%$。