ECStore: Achieving Efficient and Compressible Indexing on Outsourced Encrypted Databases
Tianxiang Shen, Ji Qi, Ning Jia, Haoze Song, Xiapu Luo, Sen Wang, Heming Cui · IEEE Transactions on Knowledge and Data Engineering · 2025
Encrypted Databases (EDBs) are essential for protecting sensitive data outsourced to public clouds, enabling diverse index-based queries over encrypted data. However, existing EDB indexes often incur high storage overhead and performance degradation, primarily due to the poor compressibility of pseudorandom encrypted values, which leads to frequent accesses to slower persistent storage as indexes outgrow main memory. We introduceECStore, the first EDB that supports compressible and efficient indexing. Observing that EDB indexes are used solely for lookups and never decrypted, we designECTree, a cryptographic hash-based index structure in which each node is a compressible bit-string identifier that conceals plaintext keys.ECTreeenables logarithmic-time encrypted search via a novel membership testing mechanism. To address false positives arising in dynamic workloads, we introduceDirected View Check(DVC), which detects inaccuracies and avoids redundant traversals. Additionally,ECTree's Merkle-tree-like structure supports encrypted query authentication, resisting server compromise. Extensive evaluations show thatECStorecan achieve up to 94.7% lower latency and 10.5x higher throughput on popular benchmarks compared to notable EDBs.