Fully Homomorphic Encryption (FHE) promises the ability to compute over encrypted data without revealing sensitive contents. However, enabling this feature for high-frequency updates and statistical analysis in outsourced databases remains elusive due to the structural mismatch between mutable database records and the cryptographically expensive mutability of FHE ciphertexts. This paper presents Hermes, a prototype system tailored for efficient global aggregation queries and dynamic tuple updates on homomorphically encrypted databases. The core design of Hermes is twofold. First, to amortize FHE costs and accelerate unconditional aggregations, Hermes introduces a data model aware of SIMD structures. Precomputed aggregate statistics become a primary element, dynamically maintained within the ciphertext to support constant time global aggregations without expensive Galois automorphisms. Second, to support mutable ciphertexts in-place, we develop data oblivious homomorphic algorithms built upon polynomial slot masking and shifting, provably secure under standard security models. Hermes is implemented as a suite of C++ loadable functions in MySQL. Extensive evaluations on the TPC-H benchmark and three real-world datasets demonstrate significant performance improvements in global query throughput, tuple insertions, and tuple deletions compared to conventional FHE implementations, validating its efficacy for highly dynamic and analytical workloads.

翻译：全同态加密（FHE）能够在不解密的情况下对加密数据进行计算，从而保护敏感内容。然而，由于可变数据库记录与FHE密文在密码学上昂贵的可变性之间存在结构性不匹配，这一特性在支持高频更新与统计分析的数据库外包场景中仍难以实现。本文提出Hermes，一个面向同态加密数据库高效全局聚合查询与动态元组更新的原型系统。Hermes的核心设计包含两方面：首先，为摊销FHE计算成本并加速无条件聚合，Hermes引入了一种感知SIMD结构的数据模型。预计算的聚合统计量成为密文中的基本元素，通过动态维护实现常数时间的全局聚合，无需昂贵的伽罗瓦自同构运算。其次，为支持密文就地更新，我们开发了基于多项式槽位掩码与移位的数据无关同态算法，并在标准安全模型下证明其安全性。Hermes以MySQL中一组C++可加载函数的形式实现。基于TPC-H基准测试与三个真实数据集的广泛评估表明，相较于传统FHE实现，Hermes在全局查询吞吐量、元组插入与删除操作上均实现了显著性能提升，验证了其在高动态与分析型负载下的有效性。