无高斯噪声的全同态加密方案

基于带舍入学习（LWR）问题，一个分级全同态加密方案最近被提出。LWR问题是带误差学习（LWE）问题的变型，但它省掉了代价高昂的高斯噪声抽样，因此与现有基于LWE问题的全同态加密方案相比，该基于LWR问题的全同态加密方案具有更高的计算效率。然而，该基于LWR问题的全同态加密方案在同态运算时需要输入用户的运算密钥。因此，基于LWR问题构造了一个新的分级全同态加密方案，该方案在同态运算时不需要输入用户的运算密钥。鉴于所提方案可应用于构造基于身份的全同态加密方案、基于属性的全同态加密方案等，它具有比最近所提出的基于LWR问题的全同态加密方案更广泛的应用场景。

Fully homomorphic encryption scheme without Gaussian noise

Much lately, a leveled fully homomorphic encryption scheme was proposed based on the Learning With Rounding (LWR) problem. The LWR problem is a variant of the Learning With Errors (LWE) problem, but it dispenses with the costly Gaussian noise sampling. Thus, compared with the existing LWE-based fully homomorphic encryption schemes, the proposed LWR-based fully homomorphic encryption scheme has much higher efficiency. But then, the user's evaluation key was needed to be obtained in the homomorphic evaluator of the proposed LWR-based fully homomorphic encryption scheme. Accordingly, a new leveled fully homomorphic encryption scheme was constructed based on the LWR problem, and the user's evaluation key was not needed to be obtained in the homomorphic evaluator of the new fully homomorphic encryption scheme. Since the new proposed fully homomorphic encryption scheme can be used to construct the schemes such as identity-based fully homomorphic encryption schemes, and attribute-based fully homomorphic encryption schemes, the new proposed scheme has wider application than the lately proposed LWR-based fully homomorphic encryption scheme.