Hermes: Efficient Global Homomorphic Aggregation over Mutable Packed Ciphertexts

TL;DR

Hermes is a system that enables efficient, high-performance aggregation and in-place updates on homomorphically encrypted databases, overcoming key limitations of prior FHE systems.

Contribution

Hermes introduces a SIMD-aware packed data model and homomorphic algorithms for in-place mutability, achieving scalable, dynamic encrypted database operations.

Findings

Significant performance improvements in query throughput.

Efficient support for tuple insertions and deletions.

Validated on TPC-H and real-world datasets.

Abstract

Fully Homomorphic Encryption (FHE) promises the ability to compute over encrypted data without revealing sensitive contents. However, enabling 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 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 SIMD-aware packed data model that embeds precomputed aggregate statistics directly into each ciphertext, enabling constant-time global aggregations without expensive Galois automorphisms. Second, to support true in-place mutability, we develop homomorphic algorithms based on polynomial slot masking and shifting, which are provably secure under the standard IND-CPA model. We scope Hermes to unconditional global aggregations to achieve both high performance and in-place updates simultaneously, two properties that prior FHE database systems have not delivered at scale. 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 query throughput, tuple insertions, and tuple deletions compared to conventional FHE implementations, validating its efficacy for highly dynamic and analytical workloads.