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.

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