论文标题
可验证的编码用于安全同构分析
Verifiable Encodings for Secure Homomorphic Analytics
论文作者
论文摘要
同态加密可以直接在密文上执行算术操作，它是保护敏感数据云规定计算隐私的有前途解决方案。但是，无法确保计算结果的正确性。我们提出了两个错误检测编码并构建身份验证器，以在不同的权衡折衷的情况下对基于云的同质计算进行实际客户验证，而不会损害加密算法的特征。我们的身份验证器以基于错误的完全同型加密方案为基础，在趋势环学习的基础上运行。我们在Veritas中实现解决方案，Veritas是一个现成的系统，用于验证通过加密数据执行的外包计算。我们表明，与先前的工作相反，Veritas支持对任何同构操作的验证，并且我们证明了它在各种应用中的实用性，例如乘车，基因组数据分析，加密搜索以及机器学习培训和推理。
Homomorphic encryption, which enables the execution of arithmetic operations directly on ciphertexts, is a promising solution for protecting privacy of cloud-delegated computations on sensitive data. However, the correctness of the computation result is not ensured. We propose two error detection encodings and build authenticators that enable practical client-verification of cloud-based homomorphic computations under different trade-offs and without compromising on the features of the encryption algorithm. Our authenticators operate on top of trending ring learning with errors based fully homomorphic encryption schemes over the integers. We implement our solution in VERITAS, a ready-to-use system for verification of outsourced computations executed over encrypted data. We show that contrary to prior work VERITAS supports verification of any homomorphic operation and we demonstrate its practicality for various applications, such as ride-hailing, genomic-data analysis, encrypted search, and machine-learning training and inference.
