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.

翻译：同态加密允许直接在密文上执行算术运算，是保护云端委托计算中敏感数据隐私的一种有前景的解决方案。然而，计算结果的正确性无法得到保证。我们提出了两种误差检测编码，并构建了认证器，使其能够在不同权衡下实现基于云的同态计算的实用客户端验证，同时不影响加密算法的特性。我们的认证器基于当前流行的基于整数环上错误学习的全同态加密方案运行。我们在VERITAS系统中实现了我们的解决方案，这是一个用于验证对加密数据执行的外包计算的即用型系统。我们证明，与先前工作不同，VERITAS支持任何同态操作的验证，并展示了其在多种应用中的实用性，例如网约车、基因组数据分析、加密搜索以及机器学习训练和推理。